Machine Learning: Machine Learning 2019 beginner
Technology:
Expertise:
- 9 Courses | 6h 5m 49s
- 9 Books | 52h 2m
- Includes Lab
- 2 Courses | 2h 56m 54s
- 2 Courses | 2h 27m 8s
- 4 Courses | 3h 12m 33s
- 9 Books | 39h 18m
- Includes Lab
- 1 Course | 44m 31s
- 1 Course | 45m 38s
- 1 Course | 2h 4m 17s
- 1 Course | 1h 27m 3s
- 3 Courses | 5h 31m 1s
- 1 Course | 1h 54m 50s
- 3 Books | 4h 41m
- 3 Courses | 1h 46m 5s
- 4 Books | 15h 27m
- Includes Lab
- 2 Courses | 1h 38m 46s
- 19 Courses | 20h 9m 16s
- 8 Books | 32h 53m
- Includes Lab
- 5 Courses | 5h 52m 8s
- 7 Books | 20h 28m
- Includes Lab
- 5 Courses | 6h 1m 20s
- 4 Books | 13h 57m
- 2 Courses | 2h 30m 22s
- 3 Courses | 2h 38m 59s
- 5 Books | 45h 4m
- 2 Courses | 1h 37m 40s
- 18 Courses | 14h 25m 5s
- 1 Book | 2h 25m
- 8 Courses | 9h 17m 2s
- 3 Books | 13h 52m
- Includes Lab
- 28 Courses | 28h 31m 21s
- 8 Books | 32h 30m
- Includes Lab
- 1 Course | 1h 15m 30s
- 6 Books | 24h 22m
- 4 Courses | 6h 10m 24s
- 4 Courses | 6h 2m 43s
- 1 Book | 6h 56m
- 3 Courses | 4h 25m 38s
- 6 Courses | 10h 22m 37s
- 2 Courses | 3h 24m 20s
- 2 Courses | 1h 39m 53s
- 2 Courses | 2h 24m 6s
Discover machine learning, where computers use algorithms to access data and learn to solve problems by themselves.
GETTING STARTED
Introduction to Machine Learning & Supervised Learning
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3m 13s
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2m 35s
GETTING STARTED
Using BigML: An Introduction to Machine Learning & BigML
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2m 34s
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7m 11s
GETTING STARTED
Machine & Deep Learning Algorithms: Introduction
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1m 58s
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8m 39s
GETTING STARTED
Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform
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1m 37s
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12m 19s
GETTING STARTED
No-code ML with RapidMiner: Getting Started with RapidMiner
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1m 38s
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11m 59s
GETTING STARTED
Machine Learning with BigQuery ML: Building Regression Models
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1m 47s
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12m 31s
GETTING STARTED
Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
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2m 26s
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4m
GETTING STARTED
Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
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1m 38s
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3m 52s
GETTING STARTED
Using BigML: Building Supervised Learning Models
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2m 38s
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8m 56s
GETTING STARTED
Bayesian Methods: Bayesian Concepts & Core Components
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1m 38s
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7m 30s
GETTING STARTED
AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
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1m 8s
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5m 40s
GETTING STARTED
TensorFlow: Introduction to Machine Learning
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2m 9s
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8m 21s
GETTING STARTED
Model Management: Building Machine Learning Models & Pipelines
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1m 37s
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4m 17s
GETTING STARTED
ML & Dimensionality Reduction: Performing Principal Component Analysis
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2m 12s
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4m 45s
GETTING STARTED
Low-code ML with KNIME: Building Regression Models
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1m 32s
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4m 57s
GETTING STARTED
No-code ML with RapidMiner: Performing Regression Analysis
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2m 7s
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4m 52s
GETTING STARTED
Machine Learning with BigQuery ML: Building Classification Models
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1m 42s
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6m 52s
GETTING STARTED
MLOps with MLflow: Creating & Tracking ML Models
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1m 18s
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10m 5s
GETTING STARTED
MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM
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2m 9s
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9m 26s
GETTING STARTED
MLOps with Data Version Control: Creating & Using DVC Pipelines
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1m 54s
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6m 43s
COURSES INCLUDED
Introduction to Machine Learning & Supervised Learning
Machine learning includes many different fields that focus on different problems. Explore what machine learning is and the fundamentals of supervised learning.
17 videos |
46m
Assessment
Badge
Supervised Learning Models
Supervised learning is one of the most popular techniques in machine learning. Explore supervised learning models and how to use them to solve problems.
13 videos |
33m
Assessment
Badge
Unsupervised Learning
Unsupervised learning can provide powerful insights on data without the need to annotate examples. Explore unsupervised learning, clustering, anomaly detection, and dimensional reduction.
12 videos |
25m
Assessment
Badge
Neural Networks
Due to recent advancements in processing, neural networks have become easier to train, which made them extremely popular. Explore neural networks and how to use them.
13 videos |
30m
Assessment
Badge
Convolutional and Recurrent Neural Networks
Some tasks aren't suitable for traditional neural networks and require specialized neural networks. Explore convolutional and recurrent neural networks and the types of problems they can solve.
13 videos |
33m
Assessment
Badge
Applying Machine Learning
Applying machine learning to problems can be a difficult task because of all the different models that are offered. Discover how to evaluate and select machine learning models and apply machine learning to a problem.
13 videos |
32m
Assessment
Badge
Building ML Training Sets: Introduction
There are numerous options available to scale and encode features and labels in data sets to get the best out of machine learning (ML) algorithms. In this 10-video course, explore techniques such as standardizing, nomalizing, and one-hot encoding. Learners begin by learning how to use Pandas library to load a data set in the form of a CSV file and perform exploratory analysis on its features. Then use scikit-learn's Binarizer to transform the continuous data in a series to binary values; apply the MiniMaxScaler on a data set to get two similar columns to have the same range of values; and standardize multiple columns in data sets with scikit-learn's StandardScaler. Examine differences between the Normalizer and other scaling techniques, and learn how to represent values in a column as a proportion of the maximum absolute value by using the MaxAbScaler. Finally, discover how to use Pandas library to one-hot encode one or more features of your data set and distinguish between this technique and label encoding. The concluding exercise involves building ML training sets.
10 videos |
1h 9m
Assessment
Badge
Building ML Training Sets: Preprocessing Datasets for Linear Regression
This 7-video course helps learners discover how to implement machine learning scaling techniques such as standardizing and min-max scaling on continuous data and one-hot encoding on categorical features to improve performance of linear regression models. In the first tutorial, you will use Pandas library to load a CSV file into a data frame and analyze its contents by using Pandas and Matplotlib. You will then learn how to create a linear regression model with scikit-learn to predict the sale price of a house and evaluate this model by using metrics such as mean squared error and r-square. Next, learners will examine the application of min-max scaling on continuous fields and one-hot encoding on the categorical columns of a data set. Then analyze effects of preprocessing by recognizing benefits of scaling and encoding data sets by evaluating the performance of a regression model built with preprocessed data. Also, learn how to use scikit-learn's StandardScaler on a data set's continuous features and compare its effects with that of min-max scaling. The concluding exercise involves preprocessing data for regression.
7 videos |
50m
Assessment
Badge
Building ML Training Sets: Preprocessing Datasets for Classification
In this course, learners can explore how to implement machine learning scaling techniques such as standardizing and normalizing on continuous data and label encoding on the target, in order to get the best out of machine learning algorithms. Examine dimensionality reduction by using Principal Component Analysis (PCA). Start this 6-video course by using Pandas library to load a CSV data set into a data frame and scale continuous features by using a standard scaler. You will then learn how to build and evaluate a support vector classifier in scikit-learn; use Pandas and Seaborn to generate a heat map; and spot the correlations between features in a data set. Discover how to apply the technique of PCA to reduce the number of dimensions in your input data and obtain the explained variance of each principal component. In the course's final tutorial, you will explore how to apply normalization and PCA on data sets and build a classification model with the principal components of scaled data. The concluding exercise involves processing data for classification.
6 videos |
43m
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
Artificial Intelligence and Machine Learning
This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.
11 videos |
1h 36m
Assessment
Badge
Deep Learning and Neural Networks
Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.
11 videos |
1h 20m
Assessment
Badge
COURSES INCLUDED
Using BigML: An Introduction to Machine Learning & BigML
From self-driving cars to predicting stock prices, machine learning has an exciting range of applications. BigML, due to its ease of use, makes these algorithms widely accessible. This course outlines machine learning fundamentals and how these are applied in BigML. You'll start by examining various machine learning algorithm categories and the kinds of problems they're used to solve. You'll then investigate the classification problem and the process involved in training and evaluating such models. Next, you'll examine linear regression and how this can help predict a continuous value. Moving on, you'll explore the concept of unsupervised learning and its application in clustering, Principal Component Analysis (PCA), and generating associations. Finally, you'll recognize how all of this comes together when using BigML to significantly simplify the building and maintenance of your machine learning models.
11 videos |
1h 10m
Assessment
Badge
Using BigML: Getting Hands-on with BigML
BigML not only provides ease-of-use, but it also offers flexibility in how you work with your data. This course serves as a hands-on introduction to BigML and its vast array of features. You'll start by exploring the different ways data can be loaded into the platform and how these can be transformed into datasets to train and test a machine learning model. You'll gain practical experience with some of the tools available to help you better understand your data - from histograms and scatterplots to visualizations of value distribution. Moving on, you'll build a fundamental classification model, a decision tree, which takes employee details and predicts whether they'll stay or leave in the next year. Finally, you'll investigate some possible configurations for this model.
11 videos |
1h 16m
Assessment
Badge
COURSES INCLUDED
Machine & Deep Learning Algorithms: Introduction
Examine fundamentals of machine learning (ML) and how Pandas ML can be used to build ML models in this 7-video course. The working of Support Vector Machines to perform classification of data are also covered. Begin by learning about different kinds of machine learning algorithms, such as regression, classification, and clustering, as well as their specific applications. Then look at the process involved in learning relationships between input and output during the training phase of ML. This leads to an introduction to Pandas ML, and the benefits of combining Pandas, scikit-learn, and XGBoost into a single library to ease the task of building and evaluating ML models. You will learn about Support Vector Machines, which are a supervised machine learning algorithm, and how they are used to find a hyperplane to divide data points into categories. Learners then study the concept of overfitting in machine learning, and the problems associated with a model overfitted to training data. and how to mitigate the issue. The course concludes with an exercise in machine learning and classification.
7 videos |
45m
Assessment
Badge
Machine & Deep Learning Algorithms: Regression & Clustering
In this 8-video course, explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models. Begin by examining application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model. Then study an introduction to regression and how it works. Next, take a look at the characteristics of regression such as simplicity and versatility, which have led to widespread adoption of this technique in a number of different fields. Learn to distinguish between supervised learning techniques such as regression and classifications, and unsupervised learning methods such as clustering. You will look at how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data. Recognize the need to reduce large data sets with many features into a handful of principal components with the PCA (Principal Component Analysis) technique. Finally, conclude the course with an exercise recalling concepts such as precision and recall, and use cases for unsupervised learning.
8 videos |
48m
Assessment
Badge
Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
Classification, regression, and clustering are some of the most commonly used machine learning (ML) techniques and there are various algorithms available for these tasks. In this 10-video course, learners can explore their application in Pandas ML. First, examine how to load data from a CSV (comma-separated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikit-learn library to build and train a LinearSVC classification model and evaluate its performance with available model evaluation functions. You will explore how to install Pandas ML and define and configure a ModelFrame, then compare training and evaluation in Pandas ML with equivalent tasks in scikit-learn. Learn how to build a linear regression model by using Pandas ML. Then evaluate a regression model by using metrics such as r-square and mean squared error, and visualize its performance with Matplotlib. Work with ModelFrames for feature extraction and encoding, and configure and build a clustering model with the K-Means algorithm, analyzing data clusters to determine unique characteristics. Finally, complete an exercise on regression, classification, and clustering.
10 videos |
1h 3m
Assessment
Badge
Automation Design & Robotics
In this 12-video course, you will examine the different uses of data science tools and the overall platform, as well as the benefits and challenges of machine learning deployment. The first tutorial explores what automation is and how it is implemented. This is followed by a look at the tasks and processes best suited for automation. This leads learners into exploring automation design, including what Display Status is, and also the Human-Computer Collaboration automation design principle. Next, you will examine the Human Intervention automation design principle; automated testing in software design and development; and also the role of task runners in software design and development. Task runners are used to automate repeatable tasks in the build process. Delve into DevOps and automated deployment in software design, development, and deployment. Finally, you will examine process automation using robotics, and in the last tutorial in the course, recognize how modern robotics and AI designs are applied. The concluding exercise involves recognizing automation and robotics design application.
13 videos |
34m
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform
Organizations have been collecting data for analytics and predictive modeling for decades, however, in the past, this analysis has been restricted to engineers and analysts who can write code. The KNIME Analytics Platform makes machine learning and data analytics more accessible by allowing you to build complex workflows with little to no code. Through this course, learn how the KNIME platform works. Examine the role of the KNIME Analytics Platform and the KNIME Community Hub. Next, explore machine learning basics and how supervised and unsupervised learning techniques work. Finally, discover how to set up the KNIME Analytics Platform and get familiar with the KNIME user interface. Upon completion, you'll be able to handle building machine learning workflows using KNIME.
7 videos |
44m
Assessment
Badge
COURSES INCLUDED
No-code ML with RapidMiner: Getting Started with RapidMiner
The more organizations depend on data for decision making, the more important machine learning becomes in every business process. The RapidMiner data science platform allows users to build complex analytics workflows with little to no code. Through this course, learn how to get started with RapidMiner. Discover what support RapidMiner offers for the analytics and artificial intelligence workflow, as well as the various tools included with RapidMiner. Next, explore the basics of machine learning and compare supervised and unsupervised learning models. Finally, work with RapidMiner Studio, and learn about the tool's different panels. Upon completion, you'll be able to set up to build predictive models in RapidMiner.
7 videos |
45m
Assessment
Badge
COURSES INCLUDED
Machine Learning with BigQuery ML: Building Regression Models
BigQuery is a flagship product on the Google Cloud Platform which allows you to build and train machine learning (ML) models using simple SQL queries. BigQuery has support for a range of supervised and unsupervised machine learning models that can be trained on data stored in BigQuery. In this course, you will be introduced to BigQuery on the Google Cloud Platform and set up a GCP trial account that allows you to work with BigQuery to train ML models. You will then review some machine learning basics and dig a little deeper into regression models. Next, you will create datasets and tables in BigQuery and upload your data to the cloud. You will visualize and explore your data using Looker Studio and prepare and clean your data using DataPrep. Finally, you will train regression models using linear regression, gradient-boosted trees, and the random forest model and evaluate and compare the performance of these models on your test data.
14 videos |
2h 4m
Assessment
Badge
COURSES INCLUDED
MLOps with MLflow: Getting Started
MLflow plays a crucial role in systemizing the machine learning (ML) workflow by providing a unified platform that seamlessly integrates different stages of the ML life cycle. In the course, you will delve into the theoretical aspects of the end-to-end machine learning workflow, covering data preprocessing and visualization. You will learn the importance of data cleaning and feature engineering to prepare datasets for model training. You will explore the MLflow platform that streamlines experiment tracking, model versioning, and deployment management, aiding in better collaboration and model reproducibility. Next, you will explore MLflow's core components, understanding their significance in data science and model deployment. You'll dive into the Model Registry that enables organized model versioning and explore MLflow Tracking as a powerful tool for logging and visualizing experiment metrics and model performance. Finally, you'll focus on practical aspects, including setting up MLflow in a virtual environment, understanding the user interface, and integrating MLflow capabilities into Jupyter notebooks.
13 videos |
1h 27m
Assessment
Badge
COURSES INCLUDED
Getting Started with MLOps
MLOps is the integration of machine learning (ML) with DevOps, focusing on streamlining the end-to-end machine learning life cycle. It emphasizes collaboration, automation, and reproducibility to deliver reliable and scalable machine learning solutions. By implementing MLOps practices, organizations can efficiently manage and govern their machine learning workflows, leading to faster development cycles, better model performance, and enhanced collaboration among data scientists and engineers. In this course, you will delve into the theoretical aspects of MLOps and understand what sets it apart from traditional software development. You will explore the factors that affect ML models in production and gain insights into the challenges and considerations of deploying machine learning solutions. Next, you will see how the Machine Learning Canvas can help you understand the components of ML development. You will then explore the end-to-end machine learning workflow, covering stages from data preparation to model deployment. Finally, you will look at the different stages in MLOps maturity in your organization, levels 0, 1, and 2. You will learn how organizations evolve in their MLOps journey and the key characteristics of each maturity level.
11 videos |
1h 27m
Assessment
Badge
MLOps with Data Version Control: Getting Started
Data Version Control (DVC) is a technology that simplifies and enhances data versioning and management. It provides Git-like capabilities to track, share, and reproduce changes in data while optimizing storage and facilitating collaboration in data-centric projects. In this course, you will discover how DVC simplifies the intricate components of ML projects - code, configuration files, data, and model artifacts. Next, you will embark on hands-on DVC exploration by installing Git locally and establishing a remote repository on GitHub. Then you will install DVC, set up a local repository, configure DVC remote storage, and add and track data using DVC. Finally, you will create Python-based machine learning (ML) models and track them with DVC and Git integration. You will create metafiles pointing to DVC-stored data and artifacts and commit these files to GitHub, tagging different model and data versions. Through Git tags, you will access specific model iterations for your work. This course will empower you with theoretical insights and practical proficiency in employing DVC and Git.
16 videos |
1h 51m
Assessment
Badge
MLOps with Data Version Control: Working with Pipelines & DVCLive
Data Version Control (DVC) pipelines enable the construction of end-to-end data processing workflows, connecting data and code stages while maintaining version control. DVCLive is a Python library for logging machine learning metrics in simple file formats and is fully compatible with DVC. In this course, you will configure and employ pipelines in DVC and modularize and coordinate each step, while leveraging the dvc.yaml file for stage management and the dvc.lock file for project consistency. Next, you will dive into practical DVC utilization with Jupyter notebooks. You will track model parameters, metrics, and artifacts via Python code's log statements using DVCLive. Then you will explore the user-friendly Iterative Studio interface. Finally, you will leverage DVCLive for comprehensive model experimentation. By pushing experiment files to DVC and employing Git branches, you will manage parallel developments. You will pull requests to streamline merging experiment branches and register model artifacts with the Iterative Studio registry. This course will equip you with the foundational knowledge of DVC and enable you to automate the tracking of model metrics and parameters with DVCLive.
17 videos |
2h 11m
Assessment
Badge
COURSES INCLUDED
Deep Learning with Keras
In this 19-video course, learners explore deep learning with Keras, including how to create and use neural networks with Keras for machine learning solutions. Begin with an overview of what neural networks are and their main components, followed by an introduction to Keras and its guiding principles. Observe how to configure Microsoft Cognitive Toolkit (CNTK) as your Keras backend; install and configure Keras; identify and work with both types of models available in Keras; and recognize features of commonly-used Keras layers and when to use them. Use Keras to make regression classifications and image classifications; Keras metrics to judge a model's performance; and Jupyter Notebooks with Keras. Next, download and load a data set from MNIST or CIFAR-10; explore data sets in Keras; prepare your data in Keras by defining input and target tensors, and compile the model in Keras. Then train and test your neural network; evaluate and score the performance of neural networks in Keras, and make predictions using your data set in Keras. The closing exercise involves using a neural network to make predictions.
19 videos |
1h 54m
Assessment
Badge
COURSES INCLUDED
Research Topics in ML & DL
This course explores research being done in machine learning and deep learning. Topics covered include neural networks and deep neural networks. First, learners examine how to prevent neural networks from overfitting. You will explore research on multilabel learning algorithms, multilabel classification, and multiple-output classifications, which are variants of the standard classification problem. Then examine deep learning algorithms, the enhanced performance of deeper neural networks that are more adept at automatic feature extraction. Next, ut facial alignment, regression tree ensembles, and deep features for scene recognition. Review ELM (Extreme Learning Machine), and how it is used to perform regression and multiclass classification.
13 videos |
41m
Assessment
Badge
Reinforcement Learning: Essentials
Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decisionmaking, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.
10 videos |
29m
Assessment
Badge
Reinforcement Learning: Tools & Frameworks
This 9-video course explores how to implement machine learning reinforcement learning by examining the terminology, including agents, the environment, state, and policy. This course demonstrates how to implement reinforcement learning by using Keras and Python; how to ensure that you can build a model; and how to launch and use Ubuntu, and VI editor to do score calculations. First, learn the role of the Markov decision process in which the agent observes the environment, with output consisting of a reward and the next state, and then acts upon it. You will explore Q-learning, a model-free reinforcement learning technique, an asynchronous dynamic programming approach, and will learn about the Q-learning rule, and Deep Q-learning. Next, learn the steps to install TensorFlow for reinforcement learning, as well as framework, which is used for reinforcement learning provided by OpenAI. Then learn how to implement TensorFlow for reinforcement learning. Finally, you will learn to implement Q-learning using Python, and then utilize capabilities of OpenAl Gym and FrozenLake.
9 videos |
34m
Assessment
Badge
COURSES INCLUDED
Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit provides powerful machine learning and deep learning algorithms for developing AI. Knowing which problems are easier to solve using Microsoft CNTK over other frameworks helps AI practitioners decide on the best software stack for a given application. In this course, you'll explore advanced techniques for working with Microsoft CNTK and identify which cases benefit most from MS CNTK. You'll examine how to load and use external data using CNTK and how to use its imperative and declarative APIs. You'll recognize how to carry out common AI development tasks using CNTK, such as working with epochs and batch sizes, model serialization, model visualization, feedforward neural networks, and machine learning model evaluation. Finally, you'll implement a series of practical AI projects using Python and MS CNTK.
15 videos |
47m
Assessment
Badge
Working With Microsoft Cognitive Toolkit (CNTK)
Microsoft Cognitive Toolkit (CNTK) is an open source framework for distributed deep learning suitable for commercial applications. It's primarily used to develop neural networks but can also be used for machine learning and cognitive computing. It supports multiple languages and can easily be used in the cloud. These factors make CNTK a good fit for various AI projects. In this course, you'll explore the basic concepts required to work with Microsoft CNTK. You'll compare other frameworks with CNTK, examine the process of creating machine learning and deep learning models with CNTK, and learn how it can be used with several cloud services. You'll move on to learn where to access CNTK documentation, community, and installation guidelines. Finally, you'll use CNTK to predict diabetes using retina scans.
16 videos |
51m
Assessment
Badge
COURSES INCLUDED
Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
Explore artificial neural networks (ANNs), their essential components, tools, and frameworks for their implementation in machine learning solutions. In this 9-video course, you will discover recurrent neural networks (RNNs) and how they are implemented. Key concepts covered here include perceptrons and the computational role they play in ANNs; learning features and characteristics of ANNs and how components are used to build a model; and learning prominent tools and frameworks used to implement sequence models and ANNs. Next, you will learn about sequence modeling as it pertains to language models; RNNs and their capabilities and components; and how to specify RNN types and their implementation features. Learners will then explore the concept of linear and nonlinear functions and classify how they are used with perceptrons; explore the concept of backpropagation and usage of backpropagation algorithm in neural networks; and examine the concept of activation functions and how linear and nonlinear activations are utilized in neural networks. Finally, you will see how to implement perceptrons with Python, and how to use modeling tools and architectures and applications of sequence models.
9 videos |
36m
Assessment
Badge
Fundamentals of Sequence Model: Language Model & Modeling Algorithms
In this 7-video course, learners can explore the concepts of language modeling, natural language processing (NLP), and sequence generation for NLP. Prominent machine learning modeling algorithms such as vanishing gradient problem, gated recurrent units (GRUs), and long short-term memory (LSTM) network are also covered. Key concepts studied in this course include language models, one of the most important parts of NLP. and how to implement NLP along with its essential components; learning the process and approach of generating sequence for NLP; and vanishing gradient problem implementation approaches to overcome the problem of taking longer times to achieve convergence. Then, learn about features and characteristics of GRUs used to resolve issues with vanishing gradient problems, and learn the problems and drawbacks of implementing short-term memory and LSTM as modeling solutions. In the concluding exercise, learners will review the essential components and prominent applications of language modeling and specify some of the solutions for vanishing gradient problems.
7 videos |
18m
Assessment
Badge
Build & Train RNNs: Neural Network Components
Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10-video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.
10 videos |
36m
Assessment
Badge
Build & Train RNNs: Implementing Recurrent Neural Networks
Learners will examine the concepts of perception, layers of perception, and backpropagation, and discover how to implement recurrent neural network by using Python, TensorFlow, and Caffe2 in this 10-video course. Begin by taking a look at the essential features and processes of implementing perception and backpropagation in machine learning neural networks. Next, you will compare single-layer perception and multilayer perception and describe the need for layer management. You will learn about the steps involved in building recurrent neural network models; building recurrent neural networks with Python and TensorFlow; implementing long short-term memory (LSTM) by using TensorFlow, and building recurrent neural networks with Caffe2. Caffe is a deep learning framework. Building deep learning language models using Keras-an open source neural network library-will be explored in the final tutorial of the course. The concluding exercise entails implementing recurrent neural networks by using TensorFlow and Caffe2 and building deep learning language models by using Keras.
10 videos |
48m
Assessment
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Convolutional Neural Networks: Fundamentals
Learners can explore the concepts of convolutional neural network (CNN); the underlying architecture, principles, and methods needed to build a CNN; and its implementation in a deep neural network. In this 12-video course, you will examine visual perception, and the ability to interpret the surrounding environment by using light in the visible spectrum. First, learn about CNN architecture; how to analyze the essential layers; and the impact of an initial choice of layers. Next, you will learn about nonlinearity in the first layer, and the need for several pooling techniques. Then learn how to implement a convolutional layer and sparse interaction. Examine the hidden layers of CNN, which are convolutional layers, ReLU (rectified linear unit) layers, or activation functions, the pooling layers, the fully connected layer, and the normalization layer. You will examine machine learning semantic segmentation to understand an image at the pixel level, and its implementation using Texton Forest and a random based classifier. Finally, this course examines Gradient Descent and its variants.
12 videos |
45m
Assessment
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Convolutional Neural Networks: Implementing & Training
This course explores machine learning convolutional neural networks (CNNs), which are popular for implementation in image and audio processing. Learners explore AI (artificial intelligence), and the issues surrounding implementation, how to approach organizational talent and strategy, and how to prepare for AI architecture in this 8-video course. You will learn to use the Google Colab tool, and to implement image recognition classifier by using CNN, Keras, and TensorFlow. Next, learn to install and implement a model, and use it for image classification. You will examine the artificial neural network ResNet (residual neural network), and how it builds on constructs known from pyramidal cells and cerebral cortex. You will also study PyTorch, an open-source machine learning library that enables fast, flexible experimentation, and efficient production through a hybrid front end, and learn to use the PyTorch ecosystem tool to develop and implement neural networks. Finally, this course demonstrates how to create a data set by using Training CNN by using PyTorch to categorize garments.
8 videos |
30m
Assessment
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Getting Started with Neural Networks: Biological & Artificial Neural Networks
Learners can explore fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks with Python, in this 12-video course. Begin with a look at characteristics of machine learning biological neural networks that inspired artificial neural networks. Then explore components of biological neural networks and the signal processing mechanism. Next, take a look at the essential components of the structure of artificial neural networks; learn to recognize the layered architecture of neural networks; and observe how to classify various computational models that can be implemented by using neural networks paradigm. Examine neurons connectivity, by describing the interconnection between neurons involving weights and fixed weights. This leads on to threshold functions in neural networks and the basic logic gates of AND, OR, and XNOR. Implement neural networks by using Python and the core libraries provided by Python for neural networks; create a neural network model using Python, Keras, and TensorFlow, and finally, view prominent neural network use cases. The concluding exercise involves implementing neural networks.
12 videos |
58m
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Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
Discover the basics of perceptrons, including single- layer and multilayer, and the roles of linear and nonlinear functions in this 10-video course. Learners will explore how to implement perceptrons and perceptron classifiers by using Python for machine learning solutions. Key concepts covered in this course include perceptrons, single-layer and multilayer perceptrons, and the computational role they play in artificial neural networks; learning the algorithms that can be used to implement single-layer perceptron training models; and exploring multilayer perceptrons and illustrating the algorithmic difference from single-layer perceptrons. Next, you will learn to classify the role of linear and nonlinear functions in perceptrons; learn how to implement perceptrons by using Python; and learn approaches and benefits of using the backpropagation algorithm in neural networks. Then learn the uses of linear and nonlinear activation functions in artificial neural networks; learn to implement a simple perceptron classifier using Python; and learn the benefits of using the backpropagation algorithm in neural networks and implement perceptrons and perceptron classifiers by using Python.
10 videos |
44m
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Training Neural Networks: Implementing the Learning Process
In this 13-video course, learners can explore how to work with machine learning frameworks and Python to implement training algorithms for neural networks. You will learn the concept and characteristics of perceptrons, a single layer neural network that aggregates the weighted sum of inputs, and returns either zero or one, and neural networks. You will then explore some of the prominent learning rules that to apply in neural networks, and the concept of supervised and unsupervised learning. Learn several types of neural network algorithms, and several training methods. Next, you will learn how to prepare and curate data by using Amazon SageMaker, and how to implement an artificial neural network training process using Python, and other prominent and essential learning algorithms to train neural networks. You will learn to use Python to train artificial neural networks, and how to use Backpropagation in Keras to implement multilayer perceptrons or neural networks. Finally, this course demonstrates how to implement regularization in multilayer perceptrons by using Keras.
13 videos |
1h 38m
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Training Neural Networks: Advanced Learning Algorithms
This 15-video course explores how to design advanced machine learning algorithms by using training patterns, pattern association, the Hebbian learning rule, and competitive learning. First, learners examine the concepts and characteristics of online and offline training techniques in implementing artificial neural networks, and different training patterns in teaching inputs that are used in implementing artificial neural networks. You will learn to manage training samples, and how to use Google Colab to implement overfitting and underfitting scenarios by using baseline models. You will examine regularization techniques to use in training artificial neural networks. This course then demonstrates how to train previously-built neural network models using Python, and the prominent training algorithms to implement pattern associations. Next, learn the architecture and algorithm associated with learning vector quantization; the essential phases involved in implementing Hebbian learning; how to implement Hebbian learning rule using Python; and the steps involved in implementing competitive learning. Finally, you will examine prominent techniques to use to optimize neural networks, and how to debug neural networks.
15 videos |
1h 40m
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Building Neural Networks: Development Principles
Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12-video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.
12 videos |
1h 20m
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Building Neural Networks: Artificial Neural Networks Using Frameworks
This 13-video course helps learners discover how to implement various neural networks scenarios by using Python, Keras, and TensorFlow for machine learning. Learn how to optimize, tune, and speed up the processes of artificial neural networks (ANN) and how to implement predictions with ANN is also covered. You will begin with a look at prominent building blocks involved in building a neural network, then recalling the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms. Learn how to build neural networks with Python and Keras for classification with Tensorflow as the backend. Discover how to build neural networks by using PyTorch; implement object image classification using neural network algorithms; and define and illustrate the use of learning rates to optimize deep learning. Examine various parameters and approaches of optimizing neural network speed; learn how to select hyperparameters and tune for dense networks by using Hyperas; and build linear models with estimators by using the capabilities of TensorFlow. Explore predicting with neural networks, temporal prediction optimization, and heterogenous prediction optimization. The concluding exercise involves building neural networks.
13 videos |
1h 54m
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Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN
In this 13-video course, you will explore the capabilities and features of convolutional networks for machine learning that make it a recommended choice for visual recognition implementation. Begin by examining the architecture and the various layers of convolutional networks, including pooling layer, convo layer, normalization layer, and fully connected layer, and defining the concept and types of filters in convolutional networks along with their usage scenarios. Learn about the approach to maximizing filter activation with Keras; define the concept of feature map in convolutional networks and illustrate the approach of visualizing feature maps; and plot the map of the first convo layer for given images, then visualize the feature map output from every block in the visual geometry group (VGG) model. Look at optimization parameters for convolutional networks, and hyperparameters for tuning and optimizing convolutional networks. Learn about applying functions on pooling layer; pooling layer operations; implementing pooling layer with Python, and implementing convo layer with Python. The concluding exercise involves plotting feature maps.
13 videos |
1h 6m
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Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
Learners can explore the machine learning concept and classification of activation functions, the limitations of Tanh and the limitations of Sigmoid, and how these limitations can be resolved using the rectified linear unit, or ReLU, along with the significant benefits afforded by ReLU, in this 10-video course. You will observe how to implement ReLU activation function in convolutional networks using Python. Next, discover the core tasks used in implementing computer vision, and developing CNN models from scratch for object image classification by using Python and Keras. Examine the concept of the fully-connected layer and its role in convolutional networks, and also the CNN training process workflow and essential elements that you need to specify during the CNN training process. The final tutorial in this course involves listing and comparing the various convolutional neural network architectures. In the concluding exercise you will recall the benefits of applying ReLU in CNNs, list the prominent CNN architectures, and implement ReLU function in convolutional networks using Python.
10 videos |
48m
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ConvNets: Introduction to Convolutional Neural Networks
Explore convolutional neural networks, their different types, and prominent use cases for machine learning, in this 10-video course. Learners will study the different layers and parameters of convolutional neural networks and their roles in implementing and addressing image recognition and classification problems. Key concepts covered in this course include the working mechanisms of convolutional neural networks, and the different types of convolutional neural networks that we can implement; and problems associated with computer vision, along with the prominent techniques to manage them. Next, you will learn about the role of neural networks and convolutional neural networks in implementing and addressing image recognition and classification problems; observe the prominent layers and parameters of convolutional neural networks for image classification; and learn to see the convolutional layer from a mathematical perspective, while recognizing the mathematical elements that enter into the convolution operations. Finally, learners will be shown how to build a convolutional neural network for image classification by using Python.
10 videos |
1h
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ConvNets: Working with Convolutional Neural Networks
Learners can explore the prominent machine learning elements that are used for computation in artificial neural networks, the concept of edge detection, and common algorithms, as well as convolution and pooling operations, and essential rules of filters and channel detection, in this 10-video course. Key concepts covered here include the architecture of neural networks, along with essential elements used for computations by focusing on Softmax classifier; how to work with ConvNetJS as a Javascript library and train deep learning models; and learning about the edge detection method, including common algorithms that are used for edge detection. Next, you will examine the series of convolution and pooling operations used to detect features; learn the involvement of math in convolutional neural networks and essential rules that are applied on filters and channel detection; and learn principles of convolutional layer, activation function, pooling layer, and fully-connected layer. Learners will observe the need for activation layers in convolutional neural networks and compare prominent activation functions for deep neural networks; and learn different approaches to improve convolution neural networks and machine learning systems.
10 videos |
42m
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Improving Neural Networks: Neural Network Performance Management
In this 12-video course, learners can explore machine learning problems that can be addressed with hyperparameters, and prominent hyperparameter tuning methods, along with problems associated with hyperparameter optimization. Key concepts covered here include the iterative workflow for machine learning problems, with a focus on essential measures and evaluation protocols; steps to improve performance of neural networks, along with impacts of data set sizes on neural network models and performance estimates; and impact of the size of training data sets on quality of mapping function and estimated performance of a fit neural network model. Next, you will learn the approaches of identifying overfitting scenarios and preventing overfitting by using regularization techniques; learn the impact of bias and variances on machine learning algorithms, and recall the approaches of fixing high bias and high variance in data sets; and see how to trade off bias variance by building and deriving an ideal learning curve by using Python. Finally, learners will observe how to test multiple models and select the right model by using Scikit-learn.
12 videos |
1h 56m
Assessment
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Improving Neural Networks: Loss Function & Optimization
Learners can explore the concept of loss function, the different types of Loss function and their impact on neural networks, and the causes of optimization problems, in this 10-video course. Examine alternatives to optimization, the prominent optimizer algorithms and their associated properties, and the concept of learning rates in neural networks for machine learning solutions. Key concepts in this course include learning loss function and listing various types of loss function; recognizing impacts of the different types of loss function on neural networks models; and learning how to calculate loss function and score by using Python. Next, learners will learn to recognize critical causes of optimization problems and essential alternatives to optimization; recall prominent optimizer algorithms, along with their properties that can be applied for optimization; and how to perform comparative optimizer analysis using Keras. Finally, discover the relevance of learning rates in optimization and various approaches of improving learning rates; and learn the approach of finding learning rate by using RMSProp optimizer.
10 videos |
1h 3m
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Improving Neural Networks: Data Scaling & Regularization
Explore how to create and optimize machine learning neural network models, scaling data, batch normalization, and internal covariate shift. Learners will discover the learning rate adaptation schedule, batch normalization, and using L1 and L2 regularization to manage overfitting problems. Key concepts covered in this 10-video course include the approach of creating deep learning network models, along with steps involved in optimizing networks, including deciding size and budget; how to implement the learning rate adaptation schedule in Keras by using SGD and specifying learning rate, epoch, and decay using Google Colab; and scaling data and the prominent data scaling methods, including data normalization and data standardization. Next, you will learn the concept of batch normalization and internal covariate shift; how to implement batch normalization using Python and TensorFlow; and the steps to implement L1 and L2 regularization to manage overfitting problems. Finally, observe how to implement gradient descent by using Python and the steps related to library import and data creation.
10 videos |
1h 37m
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Machine Learning & Data Analytics
Explore critical machine learning (ML) and deep learning concepts and the various categorizations of algorithms and their implementations using Python.
10 videos |
1h 3m
Assessment
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Supervised, Unsupervised & Deep Learning
Discover how to implement various supervised and unsupervised algorithms of machine learning using Python, with the primary focus of clustering and classification.
10 videos |
1h 30m
Assessment
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Deep Learning & Neural Network Implementation
Discover how to implement neural network with data sampling and workflow models using scikit-learn, and explore the pre and post model approaches of implementing machine learning workflows.
10 videos |
1h 2m
Assessment
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Implementing ML Algorithm Using scikit-learn
Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikit-learn.
10 videos |
1h 13m
Assessment
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Implementing Robotic Process Automation
Discover how to implement Robotic Process Automation (RPA) using Python, and explore various RPA frameworks with the practical implementation of UiPath.
10 videos |
1h 2m
Assessment
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COURSES INCLUDED
AI Fundamentals
Discover the fundamental concepts of the technologies driving artificial Intelligence (AI).
10 videos |
1h 3m
Assessment
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Machine Learning Implementation
Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.
12 videos |
1h 26m
Assessment
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Neural Network & Neuroph Framework
Discover the essential features and capabilities of Neuroph framework and Neural Networks, and also how to work with and implement Neural Networks using Neuroph framework.
16 videos |
1h 47m
Assessment
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Neural Network & NLP Implementation
Discover how to implement advanced neural network using DL4j and explore the concept of NLP and its implementation using OpenNLP Java library.
11 videos |
56m
Assessment
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Expert Systems & Reinforcement Learning
Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.
12 videos |
47m
Assessment
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COURSES INCLUDED
Using BigML: Building Supervised Learning Models
The versatility of BigML allows you to build supervised learning models without much complexity. In this course, you'll practice constructing a selection of supervised learning models using BigML. You'll start by building an ensemble of decision trees to perform binary classification. Next, you'll build a linear regression model to predict the values of homes in a particular region. You'll then train and evaluate a logistic regression model to illustrate how it can be used to solve similar problems to those solved using ensemble methods. Another BigML capability you'll explore is building a time series plot to make various forecasts. In each demonstration, you'll delve into some optional configurations for the model being trained. Lastly, you'll use the OptiML feature to find the optimal model for your data.
14 videos |
1h
Assessment
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Using BigML: Unsupervised Learning
BigML includes various unsupervised learning models used to gain insights into your data. These insights can help make pivotal business decisions or act as a starting point to build supervised learning models. In this course, you'll build several unsupervised learning models and analyze the results they produce. You'll start by creating clusters from a dataset and examining how data points within a cluster share similarities. You'll move on to uncover associations in a dataset about items purchased on an e-commerce platform. Next, you'll apply topic modeling to extract the topics discussed in a collection of texts. Following this, you'll transform a dataset containing multiple fields into a handful of principal components using Principal Component Analysis, or PCA. Finally, you'll explore the detection of anomalies in your dataset.
9 videos |
1h
Assessment
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COURSES INCLUDED
Bayesian Methods: Bayesian Concepts & Core Components
This 11-video course explores the machine learning concepts of Bayesian methods and the implementation of Bayes' theorem and methods in machine learning. Learners can examine Bayesian statistics and analysis with a focus on probability distribution and prior knowledge distribution. Begin with a look at the concept of Bayesian probability and statistical inference, then move on to the concept of Bayesian theorem and its implementation in machine learning. Next, learn about the role of probability and statistics in Bayesian analysis from the perspective of frequentist probability and subjective probability paradigms. You will examine standard probability, continuous distribution, and discrete distribution, and recall the essential elements of Bayesian statistics including prior distribution, likelihood function, and posterior inference. Recognize the implementation of prominent Bayesian methods including inference, statistical modeling, influence of prior belief, and statistical graphics. Describe prior knowledge and compare the differences between non-informative prior distribution and informative prior distribution. The steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution are also covered. The concluding exercise focuses on Bayesian statistics and analysis.
11 videos |
1h
Assessment
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Implementing Bayesian Model and Computation with PyMC
Learners can examine the concept of Bayesian learning and the different types of Bayesian models in this 12-video course. Discover how to implement Bayesian models and computations by using different approaches and PyMC for your machine learning solutions. Learners start by exploring critical features of and difficulties associated with Bayesian learning methods, and then take a look at defining the Bayesian model and classifying single-parameter, multiparameter, and hierarchical Bayesian models. Examine the features of probabilistic programming and learn to list the popular probabilistic programming languages. You will look at defining Bayesian models with PyMC and arbitrary deterministic function and generating posterior samples with PyMC models. Next, learners recall the fundamental activities involved in the PyMC Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion. Delve into the computation methods of Bayesian, including numerical integration, distributional approximation, and direct simulation. Also, look at computing with Markov chain simulation, and the prominent algorithms that can be used to find posterior modes based on the distribution approximation. The concluding exercise focuses on Bayesian modeling with PyMC.
12 videos |
47m
Assessment
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Bayesian Methods: Advanced Bayesian Computation Model
This 11-video course explores advanced Bayesian computation models, as well as how to implement Bayesian modeling with linear regression, nonlinear, probabilistic, and mixture models. In addition, learners discover how to implement Bayesian inference models with PyMC3. First, learn how to build and implement Bayesian linear regression models by using Python for machine learning solutions. Examine prominent hierarchical linear models from the perspective of regression coefficients. Then view the concept of probability models and use of Bayesian methods for problems with missing data. You will discover how to build probability models by using Python, and examine coefficient shrinkage with nonlinear models, nonparametric models, and multivariate regression from nonlinear models. Examine fundamental concepts of Gaussian process models; the approaches of classification with mixture models and regression with mixture models; and essential properties of Dirichlet process models. Finally, learn how to implement Bayesian inference models in Python with PyMC3. The concluding exercise recalls hierarchical linear models from the perspective of regression coefficients, and asks learners to describe the approach of working with generalized linear models, and implement Bayesian inference by using PyMC3.
11 videos |
51m
Assessment
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COURSES INCLUDED
Keras - a Neural Network Framework
Keras is a deep learning package suitable for beginners. Although it is applied in multiple standard deep learning use cases, it is also used by commercial-grade products. To facilitate this, Keras provides additional, flexible options on top of the well-known Sequential API, which allow you to customize and create various neural networks. To utilize this, however, requires a more in-depth knowledge of the Keras framework. In this course, you'll develop the core skills needed to work with the Keras framework. You'll explore the advantages and disadvantages of using Keras over other frameworks, and examine how Keras can be used with TensorFlow. You'll move on to recognize how Keras is used for machine learning and deep learning. Finally, you'll implement two deep learning projects using the Keras framework.
15 videos |
47m
Assessment
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Working With the Keras Framework
Keras provides a quick way to implement, train, and evaluate robust neural networks in Python. Using Keras for AI development for prototyping AI is standard practice and AI practitioners need to know why and how to use Keras for particular AI implementations. In this course, you'll explore advanced techniques for working with the Keras framework. You'll recognize how Keras is different from other AI frameworks and identify cases in which it is advantageous to use Keras. You'll examine the functionality of the Keras Sequential model and Functional API and the role of multiple deep learning layers present in Keras. Finally, you will work with practical AI projects developed using Keras and troubleshoot common problems related to model training and evaluation.
16 videos |
50m
Assessment
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COURSES INCLUDED
AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
Machine learning (ML) has become indispensable across all industries. With staggering amounts of data generated globally every second, it's impossible to make sense of it without using such advanced data analytics. The AWS Certified Machine Learning - Specialty certification is one of the most coveted yet challenging certs a data engineer or scientist can get. To pass the associated exam, candidates must demonstrate knowledge of various machine learning concepts and the ability to solve real-world business challenges. Use this course to prepare for acquiring this valuable certification. Get to grips with key data engineering and machine learning terminology, concepts, tools, tasks, and workflows. Then, dive into how the AWS Machine Learning platform is used for real-world applications. Upon completing this course, you'll recognize key ML concepts and how to prepare datasets, develop ML models, and optimize models for improved predictive accuracy.
12 videos |
35m
Assessment
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AWS Certified Machine Learning: Amazon S3 Simple Storage Service
Amazon Simple Storage Service (S3) is widely used for many machine learning applications. Using Amazon S3, you can quickly and easily run machine learning algorithms on large databases using remote machines. In this course, you'll explore the various data formats Amazon S3 uses for machine learning pipelines. You'll then examine several Amazon S3 services in detail, looking at their use cases, workflows, and features. You'll also learn about the vital Amazon S3 functionalities related to security and access management and data storage, archiving, and analytics. When you've finished this course, you'll be able to outline how Amazon S3 is used for machine learning tasks, taking you one step closer to being fully prepared for the AWS Certified Machine Learning - Specialty exam.
12 videos |
22m
Assessment
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AWS Certified Machine Learning: Data Movement
As the amount of data being collected has exploded, it has become crucial for businesses to rapidly access, transform, and analyze data. From the traditional batch processing to the ever-evolving real-time data analytics, AWS has various tools to handle large volumes of data and perform real-time analytics to ensure high-service uptimes and personalize recommendations. Explore various Amazon tools like AWS Glue, AWS Data Catalog, and AWS Kinesis using this course. These tools are commonly used for data movement. This course will also help you understand how these processes function on the AWS platform and familiarize you with the data movement workflows. Data movement and processing are at the core of any data analysis, and after completing this course, you'll be familiar with multiple tools and approaches that can be used to conveniently transform raw data, combine databases, and stream data, Further, you'll be able to prepare for the AWS Certified Machine Learning - Specialty certification.
14 videos |
34m
Assessment
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AWS Certified Machine Learning: Data Pipelines & Workflows
Creating a data pipeline is essential to making any data-related product. AWS Data Pipeline, AWS Batch, and AWS Workflow frameworks allow you to manage data using ETL data management across various AWS tools and services, making AWS a perfect platform for combining data from multiple sources. In this course, you'll learn how to automate data movement and transformation processes on AWS and define data-driven pipelines and workflows. Investigating how data pipelines enable seamless, scalable, and fault-tolerant data transfer between AWS storage and computational tools helps illuminate the full potential of AWS in machine learning. By the end of this course, you'll have a working knowledge of the most common use cases of AWS Data Pipeline, AWS Batch, and AWS Workflow, bringing you closer to being fully prepared for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
40m
Assessment
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AWS Certified Machine Learning: Jupyter Notebook & Python
Exploring and analyzing data to comprehend its underlying characteristics and patterns becomes increasingly vital as vaster amounts are collected. This is key in formulating the most suitable problems, the solving of which helps achieve real-world business goals. Use this course to get your head around the programming fundamentals for machine learning in AWS, which form the basis for most data exploratory steps on the AWS platform. Explore various Python packages used in machine learning and data analysis and become familiar with Jupyter Notebook's fundamental concepts. Then, work with Python and Jupyter Notebook to create a machine learning model. When you're done, you'll be able to use Jupyter Notebook and various Python packages in machine learning and data analysis. You'll be one step closer to being prepared for the AWS Certified Machine Learning - Specialty certification exam.
13 videos |
38m
Assessment
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AWS Certified Machine Learning: Data Analysis Fundamentals
Data Analysis is a primary method for deriving valuable insight from raw and unstructured data. The appropriate application of data analysis techniques is vital in deriving only the relevant insight and factual knowledge from available data. Picking the correct data distribution or visualization technique can become critical to the overall data analysis results. Using this course, become familiar with the core foundations of data - the essential ground for any data analysis and machine learning operation. Examine the various types of data that exist, inherent data distributions, both traditional and modern methods of visualizing data, and how time series analysis works. When you've completed this course, you'll be able to describe the core concepts of data analysis and implement some valuable visualization and analysis techniques using Python. This course will prepare you for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
34m
Assessment
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AWS Certified Machine Learning: Athena, QuickSight, & EMR
Amazon offers a wide range of services that help enhance AWS workflows, making it much easier to create automated data processing and machine learning pipelines. Use this course to get to grips with some of these services. Explore how Amazon Athena is used for querying data and how Amazon QuickSight integrates with Athena to help decision-makers analyze data and interpret information in an interactive visual environment. Then, get hands-on practice working with both tools. Moving along, learn how Amazon EMR is used to process large amounts of data and investigate its integrations with various Apache frameworks, such as Hadoop and Spark. When you're done, you'll know how to use Amazon services to automate machine learning processes, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
13 videos |
36m
Assessment
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AWS Certified Machine Learning: Feature Engineering Overview
Feature engineering is key in extracting the right attributes from raw incoming data, which is fundamental in building reliable ML algorithms. Amazon SageMaker, a fully managed machine learning studio on AWS, provides feature engineering functionality and many other machine-learning-related tasks. Use this course to explore fundamental feature engineering concepts and learn how to use Amazon SageMaker for feature engineering tasks. Work with the various tools available in SageMaker for preparing data for ML models, such as Ground Truth (for labeling data) and Feature Store (for storing, retrieving, and sharing features). Moving along, investigate various deficiencies, such as missing values, imbalance, and outliers, in real-world data and learn how to address these challenges. Upon completion, you'll be able to carry out feature engineering tasks efficiently using Amazon SageMaker, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
34m
Assessment
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AWS Certified Machine Learning: Feature Engineering Techniques
Raw data is typically not perfect for developing effective machine learning (ML) models. Often, it needs to be processed using various feature engineering techniques to make it more suitable for building accurate and optimized ML models. Take this course to learn about techniques that help prepare the data to be compatible and improve the performance of machine learning models. Investigate techniques that are used to improve data usability, such as one-hot encoding, binning, transformations, scaling, and shuffling. You will also learn about the importance and usage of text feature engineering and major workflows in the AWS environment. After completing this course, you'll be able to implement feature engineering techniques using AWS workflows, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
13 videos |
28m
Assessment
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AWS Certified Machine Learning: Problem Framing & Algorithm Selection
Problem framing and algorithm selection is the most important part of any machine learning (ML) project. ML engineers have to apply appropriate techniques that will result in expected prediction behavior. It is important to fully understand a particular task and choose among all the available methods and toolkits before implementing a machine learning project. Use this course to learn more about the ML mindset, discover how goal-oriented business problems can be formulated as machine learning problems, and describe factors that drive the selection of the correct algorithm for a particular scenario. The course will also help you refresh important ML concepts and terminologies. After completing this course, you'll be able to implement machine learning solutions to solve business problems, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
1h 7m
Assessment
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AWS Certified Machine Learning: Machine Learning in SageMaker
Amazon SageMaker provides broad-set capabilities for machine learning (ML) as it helps to prepare, train, and quickly deploy ML models. Use this course to learn more about the basic capabilities of SageMaker and work with it to implement solutions to various machine learning problems. Explore features and functionalities of SageMaker through practical demos and discover how to implement hyperparameter tuning. This course will also help you explore algorithms in SageMaker, such as linear learner, XGBoost, object detection, and semantic segmentation. After completing this course, you'll be able to train and tune a range of algorithms in order to solve simple classification tasks for natural language processing (NLP) and computer vision.
12 videos |
1h 27m
Assessment
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AWS Certified Machine Learning: ML Algorithms in SageMaker
Amazon SageMaker is a comprehensive machine learning (ML) toolkit that provides a broad range of functions and can be used for multiple use cases and tasks, making it an ultimate package for ML. Dive deeper into SageMaker's built-in algorithms for solving problems, such as time series forecast, clustering, and anomaly detection through this course. Examine various functionalities available in Amazon SageMaker and learn how to implement different ML algorithms. Once you have completed this course, you'll be able to use SageMaker's machine learning algorithms for your business case and be a step further in preparing for the AWS Certified Machine Learning - Specialty certification exam.
15 videos |
1h 36m
Assessment
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AWS Certified Machine Learning: Advanced SageMaker Functionality
Amazon SageMaker can be used with multiple other frameworks and toolkits to precisely define machine learning (ML) algorithms and train models and is not limited to a specific set of algorithms for ML. SageMaker also provides a wide range of tools that can be used for incremental training, distributed training, debugging, or explainability. Use this course to learn about advanced SageMaker functionality, including supported frameworks, Amazon EMR, and autoML. You'll also gain hands-on experience in using new features, such as SageMaker Experiments, SageMaker Debugger, Bias Detection, and Explainability. Once you have finished this course, you'll have the skills and knowledge to implement SageMaker's advanced functionalities. Further, you'll be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam.
13 videos |
1h 23m
Assessment
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AWS Certified Machine Learning: AI/ML Services
Amazon offers a variety of high-level no-code services for specialized machine learning (ML) tasks. These services are primarily used to implement complex pre-built algorithms for using ML with textual and visual information. Use this course to learn more about these services. Use this course to explore services, such as Amazon Kendra, Transcribe, Polly, Rekognition, Personalize, and Textract in greater detail. You'll also delve into other AWS AI/ML services through case studies. After you're done with this course, you'll be able to describe the use cases of these services and have a general overview of how to combine multiple AWS AI/ML services to work within a single application. Moreover, you'll be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam
12 videos |
1h 8m
Assessment
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AWS Certified Machine Learning: Problem Formulation & Data Collection
In order to build machine learning (ML) applications, it is important to formulate problems and collect data. Examine the choice between the online and on-premise implementation of the problem formulation and data collection phases through this course. Explore how SageMaker algorithms help complete ML projects efficiently and work with various approaches that implement recommender systems. You'll also investigate how and when to use AWS data storage services and learn more about analyzing dataset readiness. After taking this course, you'll be able to describe the advantages and disadvantages of using the cloud over an on-premise solution and define the problem formulation and success evaluation processes. You'll also be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
37m
Assessment
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AWS Certified Machine Learning: Data Preparation & SageMaker Security
Building successful machine learning (ML) applications require the transformation of raw data, such that it meets the requirements of individual ML algorithms. Explore how to prepare data using Amazon SageMaker and S3 and create security services for this data through this course. Start by delving deeper into summary statistics and visualization before moving on to security best practices for Amazon SageMaker. You'll also examine Amazon CloudWatch and Amazon CloudTrail in greater detail. After taking this course, you'll have a solid grasp of various data formats, data security practices, and monitoring and alerting services used in SageMaker. You'll also have the knowledge to prepare data for machine learning and take a step further in your preparation for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
37m
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AWS Certified Machine Learning: Model Training & Evaluation
Training a machine learning (ML) model is the first step of many when developing ML applications that enable businesses to discover new trends within broad and diverse data sets. Use this course to learn more about SageMaker's built-in algorithm and perform model training, evaluation, monitoring, tuning, and deployment using Amazon Elastic Compute Cloud (EC2) instances. Begin by examining factorization machines and the selection of EC2 instances. Next, you'll discover how to perform model training, evaluation, and deployment. You'll wrap up the course by exploring the steps involved in tuning and testing ML models. After you're done with this course, you'll have the skills and knowledge to successfully train and evaluate a model, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
30m
Assessment
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AWS Certified Machine Learning: AI Services & SageMaker Applications
Integrating AWS AI services and SageMaker with any machine learning (ML) or deep learning project is a great way to enhance its capabilities. Through this course, learn more about the additional AWS AI Services that are ready to use in the form of direct API without the need to train any ML models and dive deeper into more SageMaker functionality. Get familiar with AWS AI services that can be fully integrated into your applications in minutes. This course will also introduce you to some pre-trained algorithms in SageMaker for building high-performance natural language processing (NLP) and computer vision apps using fine-tuning techniques. After completing this course, you'll be able to identify several AI services that can be used as APIs in AWS and describe SageMaker's extensive capabilities in handling text and images. You'll also be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam.
12 videos |
49m
Assessment
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COURSES INCLUDED
TensorFlow: Introduction to Machine Learning
Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.
19 videos |
1h 40m
Assessment
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TensorFlow: Simple Regression & Classification Models
Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.
19 videos |
1h 36m
Assessment
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TensorFlow: Deep Neural Networks & Image Classification Using Estimators
Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.
15 videos |
1h 11m
Assessment
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TensorFlow: Convolutional Neural Networks for Image Classification
Examine how to work with Convolutional Neural Networks, and discover how to leverage TensorFlow to build custom CNN models for working with images.
17 videos |
1h 21m
Assessment
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TensorFlow: Word Embeddings & Recurrent Neural Networks
Explore how to model language and text with word embeddings and how to use those embeddings in Recurrent Neural Networks. Leveraging TensorFlow to build custom RNN models is also covered.
11 videos |
42m
Assessment
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TensorFlow: Sentiment Analysis with Recurrent Neural Networks
Discover how to construct neural networks for sentiment analysis. How to generate word embeddings on training data and use pre-trained word vectors for sentiment analysis is also covered.
12 videos |
57m
Assessment
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TensorFlow: K-means Clustering
Discover how to differentiate between supervised and unsupervised machine learning techniques. The construction of clustering models and their application to classification problems is also covered.
15 videos |
59m
Assessment
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TensorFlow: Building Autoencoders
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
10 videos |
46m
Assessment
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COURSES INCLUDED
Model Management: Building Machine Learning Models & Pipelines
In this course, you will explore various approaches to building and implementing machine learning (ML) models and pipelines and will learn how to manage classification and regression problems. Begin this 11-video course by taking a look at the differences between ML models and ML algorithms. You will go on to learn about the different types of ML models and will then explore the approaches to developing and building them. Discover how to create and save ML models by using scikit-learn, and learn to recognize the various models that can be used to manage classification and regression problems. Explore how to build ML pipelines and then examine the prominent tools that can be used. You will learn how to implement scikit-learn ML pipelines, and in the final tutorial, learners will recall the steps involved in iterative machine learning model management and the associated benefits. In the concluding exercise, you will be asked to build ML models and pipelines by using scikit-learn.
11 videos |
31m
Assessment
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Model Management: Building & Deploying Machine Learning Models in Production
In this 14-video course, learners can explore hyperparameter tuning, versioning machine learning (ML) models, and preparing and deploying ML models in production. Begin the course by describing hyperparameter and the different types of hyperparameter tuning methods, and also learn about grid search hyperparameter tuning. Next, learn to recognize the essential aspects of a reproducible study; list ML metrics that can be used to evaluate ML algorithms; learn about the relevance of versioning ML models, and implement Git and DVC machine learning model versioning. Describe ModelDB architecture used for managing ML models, and list the essential features of the model management framework. Observe how to set up Studio.ml to manage ML models and create ML models in production, and examine Flask machine learning model setup for production. Explore how to deploy machine or deep learning models in production. The exercise involves tuning hyperparameter with grid search, versioning ML models by using Git, and creating ML models for production.
14 videos |
55m
Assessment
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Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
The imbalanced-learn library that integrates with Pandas ML (machine learning) offers several techniques to address the imbalance in datasets used for classification. In this course, explore oversampling, undersampling, and a combination of techniques. Begin by using Pandas ML to explore a data set in which samples are not evenly distributed across target classes. Then apply the technique of oversampling with the RandomOverSampler class in the imbalanced-learn library; build a classification model with oversampled data; and evaluate its performance. Next, learn how to create a balanced data set with the Synthetic Minority Oversampling Technique and how to perform undersampling operations on a data set by applying Near Miss, Cluster Centroids, and Neighborhood cleaning rules techniques. Next, look at ensemble classifiers for imbalanced data, applying combination samplers for imbalanced data, and finding correlations in a data set. Learn how to build a multilabel classification model, explore the use of principal component analysis, or PCA, and how to combine use of oversampling and PCA in building a classification model. The exercise involves working with imbalanced data sets.
12 videos |
1h 23m
Assessment
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Technology Landscape & Tools for Data Management
This Skillsoft Aspire course explores various tools you can utilize to get better data analytics for your organization. You will learn the important factors to consider when selecting tools, velocity, the rate of incoming data, volume, the storage capacity or medium, and the diversified nature of data in different formats. This course discusses the various tools available to provide the capability of implementing machine learning, deep learning, and to provide AI capabilities for better data analytics. The following tools are discussed: TensorFlow, Theano, Torch, Caffe, Microsoft cognitive tool, OpenAI, DMTK from Microsoft, Apache SINGA, FeatureFu, DL4J from Java, Neon, and Chainer. You will learn to use SCIKIT-learn, a machine learning library for Python, to implement machine learning, and how to use machine learning in data analytics. This course covers how to recognize the capabilities provided by Python and R in the data management cycle. Learners will explore Python; the libraries NumPy, SciPy, Pandas to manage data structures; and StatsModels. Finally, you will examine the capabilities of machine learning implementation in the cloud.
9 videos |
26m
Assessment
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Machine Learning & Deep Learning Tools in the Cloud
This Skillsoft Aspire course explores the machine learning solutions provided by AWS (Amazon Web Services) and Microsoft, and how to compare the tools and frameworks that can be used to implement machine learning, and deep learning. You will learn to become efficient in data wrangling by building a foundation with data tools and technology. This course explores Machine Learning Toolkit provided by Microsoft, which provides various algorithms and applies artificial intelligence and deep learning. Learners will also examine Distributed Machine Learning Toolkit, which is hosted on Azure. Next, explore the machine learning tools provided by AWS, including DeepRacer and DeepLens which provide deep learning capabilities. You will learn how to use Amazon SageMaker, and how Jupyter notebooks are used to test machine learning algorithms. You will learn about other AWS tools, including TensorFlow, Apache MXNet, and Deep Learning AMI. Finally, learn about different tools for data mining and analytics, and how to build and process a data pipeline provided by KNIME (Konstanz Information Miner).
9 videos |
22m
Assessment
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ML Algorithms: Multivariate Calculation & Algorithms
Learners can explore the role of multivariate calculus in machine learning (ML), and how to apply math to data science, ML, and deep learning, in this 10-video course examining several ML algorithms, and showing how to identify different types of variables. First, learners will observe how to implement multivariate calculus, derive function representations of calculus, and utilize differentiation and linear algebra to optimize ML algorithms. Next, you will examine how to use advanced calculus and discrete optimization, to implement robust, and high-performance ML applications. Then you will learn to use R and Python to implement multivariate calculus for ML and data science. You will learn about partial differentiation, and its application on vector calculus and differential geometry, and the use of product rule and chain rule. You will examine the role of linear algebra in ML, and learn to classify the techniques of optimization by using gradient and Jacobian matrix. Finally, you will explore Taylor's theorem and the conditions for local minimum.
10 videos |
38m
Assessment
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ML Algorithms: Machine Learning Implementation Using Calculus & Probability
This course explores the use of multivariate calculus, derivative function representations, differentiation, and linear algebra to optimize ML (machine learning) algorithms. In 10 videos, learners will observe how to use probability theory to enable prediction and other analytical types in ML, including the role of probability in chain rule and Bayes' rule. First, you will explore the concepts of variance, covariance, and random vectors, before examining Likelihood and Posteriori estimation. Next, learn how to use estimation parameters to determine the best value of model parameters through data assimilation, and how it can be applied to ML. You will explore the role of calculus in deep learning, and the importance of derivatives in deep learning. Continue by learning optimization functions such as gradient descent, and whether to increase or decrease weight to maximize or minimize some metrics. You will learn to implement differentiation and integration in R and how to implement calculus derivatives, integrals using Python. Finally, you will examine the use of limits and series expansion in Python.
10 videos |
30m
Assessment
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NLP for ML with Python: NLP Using Python & NLTK
This course explores how natural language processing (NLP) is used for machine learning, and examines the benefits and challenges of NLP when creating an application that can essentially understand human language. In its 13 videos, learners will be shown the essential components of NLP, including parsers, corpus, and corpus linguistic, as well as how to implement regular expressions. This course goes on to examine tokenization, a way to separate a piece of text into smaller units, and then illustrates different tokenization use cases with NLTK (Natural Language Toolkit). You will learn to use stop words using libraries and the NLTK. This course demonstrates how to implement regular expressions in Python to build NLP-powered applications. Learners will examine the list of Python NLP libraries along with their essential capabilities, including NLTK, Gensim, CoreNLP, spaCy and PyNLPl. You will learn to set up and configure an NLTK environment to illustrate how to process raw text. Finally, this course demonstrates the use of filtering stopwords in a tokenized sentence using NLTK.
13 videos |
1h 1m
Assessment
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NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn
This 11-video course explores NLP (natural language processing) by discussing differences between stemming, a process of reducing a word to its word stem, and lemmatization, or returning the base or dictionary form of a word. Key concepts covered here include how to extract synonyms, antonyms, and topic, and how to process and analyze texts for machine learning. You will learn to use Apache's Natural Language Toolkit (NLTK), spaCy, and Scikit-learn to implement text classification and sentiment analysis. This course demonstrates the use of advanced calculus and discrete optimization to implement robust and high-performance machine learning applications. You will learn to use R and Python to implement multivariate calculus for machine learning and data science, then examine the role of probability, variance, and random vectors in implementing machine learning processes and algorithms. Finally, you will examine the role of calculus in deep learning; watch a demonstration of how to apply calculus and differentiation using R and Python libraries; see how to implement calculus, derivatives, and integrals using Python; and learn uses of limits and series expansions in Python.
11 videos |
40m
Assessment
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Linear Algebra and Probability: Fundamentals of Linear Algebra
Explore the fundamentals of linear algebra, including characteristics and its role in machine learning, in this 13-video course. Learners can examine important concepts associated with linear algebra, such as the class of spaces, types of vector space, vector norms, linear product vector and theorems, and various operations that can be performed on matrix. Key concepts examined in this course include important classes of spaces associated with linear algebra; features of vector spaces and the different types of vector spaces and their application in distribution and Fourier analysis; and inner product spaces and the various theorems that are applied on inner product spaces. Next, you will learn how to implement vector arithmetic by using Python; learn how to implement vector scalar multiplication with Python; and learn the concept and different types of vector norms. Finally, learn how to implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication by using Python; and learn about matrix decomposition and the roles of Eigenvectors and Eigenvalues in machine learning.
13 videos |
1h 40m
Assessment
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Linear Algebra & Probability: Advanced Linear Algebra
Learners will discover how to apply advanced linear algebra and its principles to derive machine learning implementations in this 14-video course. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. Key concepts covered here include how to use Python libraries to implement principal component analysis with matrix multiplication; sparse matrix and its operations; tensors in linear algebra and arithmetic operations that can be applied; and how to implement Hadamard product on tensors by using Python. Next, learn how to calculate singular-value decomposition and reconstruct a rectangular matrix; learn the characteristics of probability applicable in machine learning; and study probability in linear algebra and its role in machine learning. You will learn types of random variables and functions used to manage random numbers in probability; examine the concept and characteristics of central limit theorem and means and learn common usage scenarios; and examine the concept of parameter estimation and Gaussian distribution. Finally, learn the characteristics of binomial distribution with real-time examples.
14 videos |
1h 42m
Assessment
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Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
Explore the concept of deep learning, including a comparison between machine learning and deep learning (ML/DL) in this 12-video course. Learners will examine the various phases of ML/DL workflows involved in building deep learning networks; recall the essential components of building and applying deep learning networks; and take a look at the prominent frameworks that can be used to simplify building ML/DL applications. You will then observe how to use the Caffe2 framework for implementing recurrent convolutional neural networks; write PyTorch code to generate images using autoencoders; and implement deep neural networks by using Python and Keras. Next, compare the prominent platforms and frameworks that can be used to simplify deep learning implementations; identify and select the best fit frameworks for prominent ML/DL use cases; and learn how to recognize challenges and strategies associated with debugging deep learning networks and algorithms. The closing exercise involves identifying the steps of ML workflow, deep learning frameworks, and strategies for debugging deep learning networks.
12 videos |
58m
Assessment
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Implementing Deep Learning: Optimized Deep Learning Applications
This 11-video course explores the concepts of computational graphics, interfaces for programming graphics processing units (GPUs), and TensorFlow Extended and its pipeline components. Learners discover features and elements that should be considered for machine learning when building deep learning (DL) models, as well as hyperparameters that can be tuned to optimize DL models. Begin by examining the concept of computational graphs and recognize essential computational graph operations used in implementing DL. Then learn to list prominent processors with specialized purpose and architectures used in implementing DL. Recall prominent interfaces for programming GPUs with focus on Compute Unified Device Architecture (CUDA) and OpenCL, and then take a look at TensorFlow Extended (TFX) and TFX pipeline components for machine learning pipelines. Discover how to setup the TFX environment; use the ExampleGen and StatisticsGen TFX pipeline components to build pipelines; work with TensorFlow Model analysis; and explore the practical considerations for DL build and train. Finally, recall essential hyperparameters of DL algorithms that can be tuned to optimize DL models. The concluding exercise involves optimizing DL applications.
11 videos |
42m
Assessment
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Refactoring ML/DL Algorithms: Techniques & Principles
Explore techniques of refactoring code, the process of changing a computer program source code without modifying its external functional behavior, in this 14-video course exploring design patterns and challenges in refactoring code. First, learn the essential machine learning principles when planning code, including how to identify what instead of how, and to look for consistencies. You will then learn to recognize the causes of technical debts that contribute to challenges in existing code. Next, you will learn code refactoring techniques and types of processes that you can use to eliminate deficiencies in the code. This course demonstrates the refactoring capabilities provided by PyCharm to refactor Python code, and the steps involved in optimizing Python code. You will learn static code analysis of Python by using Prospector, refactoring code to ensure backward compatibility, and the role of design patterns in code refactoring, and how to use rope to refactor Python code. Finally, you will learn to recall the prominent antipatterns that potentially complicate code and code refactoring.
14 videos |
1h 5m
Assessment
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Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
This course explores how to select the appropriate algorithm for machine learning (ML), the principles of designing machine learning algorithms, and how to refactor machine ML code. In 11 videos, you will learn the steps involved in designing ML algorithms. The complexity in the algorithm is huge, and learners will observe how to write iterative and incremental code, and how to apply refactoring to it. This course next examines the types of ML problems, and classifies it into four categories, and how to classify machine learning algorithms. You will learn how to refactor existing ML code written in Python, and to launch and use PyCharm IDE. This course also demonstrates how to use PyCharm IDE on a specific project learners will create. You will examine the problems associated with technical debt in ML implementation, and how to manage it. Then you will learn to use SonarQube to build code coverage for machine learning code that are written in Python. Finally, this course examines automatic clone recommendations for refactoring, based on the present and the past.
11 videos |
58m
Assessment
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ML/DL Best Practices: Machine Learning Workflow Best Practices
This 12-video course explores essential phases of machine learning (ML), deep learning workflows, and data workflows that can be used to develop ML models. You will learn the best practices to build robust ML systems, and examine the challenges of debugging models. Begin the course by learning the importance of the data structure for ML accuracy and feature extraction that is wanted from the data. Next, you will learn to use checklists to develop and implement end-to-end ML and deep learning workflows and models. Learners will explore what factors to consider when debugging, and how to use flip points to debug a trained machine model. You will learn to identify and fix issues associated with training, generalizing, and optimizing ML models. This course demonstrates how to use the various phases of machine learning and data workflows that can be used to achieve key milestones of machine learning projects. Finally, you will learn high level-deep learning strategies, and the common design choices for implementing deep learning projects.
12 videos |
52m
Assessment
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ML/DL Best Practices: Building Pipelines with Applied Rules
This course examines how to troubleshoot deep learning models, and build robust deep learning solutions. In 13 videos, learners will explore the technical challenges of managing diversified kinds of data with ML (machine learning), and how to work with its challenges. This course uses case studies to demonstrate the impact of adopting deep learning best practices, and how to deploy deep learning solutions in an enterprise. First, you will learn the best approach for architecting, building, and implementing scalable ML services, and rules to build ML pipelines into applications. Then learners will examine critical challenges and patterns associated with deploying deep learning solutions in an enterprise. You will learn to use feature engineering to apply rules and features in an application, and how to use feature engineering to manage slowed growth, training-serving skew, optimization refinement, and complex models in ML application management. Finally, you will examine the checklists that are recommended for project managers to prepare and adopt when implementing machine learning.
13 videos |
1h 3m
Assessment
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Enterprise Services: Enterprise Machine Learning with AWS
This course explores features and operational benefits of using a cloud platform to implement machine learning (ML). In this 15-video course, learners observe how to manage diversified kinds of data, and the exponential growth of unstructured and structured data. First, you will examine ML workflow and compare differences between ML model development and traditional enterprise software development. Then you will learn how to use the ML services provided by AWS (Amazon Web Services) to implement end-to-end ML solutions at scale. Next, learners will examine AWS ML tools, services, and capabilities, the architecture, and internal components in Amazon SageMaker. You will continue by learning how to use Amazon Machine Learning Console to create data sources, implement ML models, and to use the models to facilitate predictions. This course compares the ML implementation scenarios and solutions in AWS, Microsoft Azure, and Google Cloud, and helps learners identify the best fit for any given scenario. Finally, you learn to use SageMaker and SageMaker Neo to create, train, tune, and deploy ML models anywhere.
15 videos |
1h 13m
Assessment
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Enterprise Services: Machine Learning Implementation on Microsoft Azure
Explore the features and operational benefits of using a cloud platform to implement ML (machine learning) by using Microsoft Azure and Amazon SageMaker, in this 14-video course. First, you will learn how to use Microsoft Azure ML tools, services, and capabilities, and how to examine MLOps (machine learning and operations) to manage, deploy, and monitor models for quality and consistency. You will create Azure Machine Learning workspaces, and learn to configure development environments, build, and manage ML pipelines, to work with data sets, train models, and projects. You will develop and deploy predictive analytic solutions using the Azure Machine Learning Service visual interface, and work with Azure Machine Learning R Notebooks to fit and publish models. You will learn to enable CI/CD (continuous integration and continuous delivery) with Azure Pipelines, and examine ML tools in AWS (Amazon Web Services) SageMaker, and how to use Amazon's ML console. Finally, you will learn to track code from Azure Repos or GitHub, trigger release pipelines, and automate ML deployments by using Azure Pipelines.
14 videos |
1h 12m
Assessment
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Enterprise Services: Machine Learning Implementation on Google Cloud Platform
This course explores the GCP (Google Cloud Platform) machine learning (ML) tools, services, and capabilities, and different stages in the Google Cloud Platform machine learning workflow. This 14-video course demonstrates a high-level overview of different stages in Google Cloud Platform machine learning workflow. You will examine the features of BigQuery, and how to use Big Query ML to create and evaluate a binary logistic regression model using Big Query ML statement. Next, learners will observe how to use the Google AI Platform and Google Cloud AutoML components and features used for training, evaluating, and deploying ML models. You will learn to train models by using the built-in linear learner algorithm, submit jobs with GCloud and Console, create and evaluate binary logistic regression models, and set up and work with Cloud Datalab. You will learn to use AutoML Tables to work with data sets, to train machine learning models for predictions. Finally, you will work with Google Cloud AutoML Natural Language to create custom ML models for content category classification.
14 videos |
1h
Assessment
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Advanced Reinforcement Learning: Principles
This 11-video course delves into machine learning reinforcement learning concepts, including terms used to formulate problems and workflows, prominent use cases and implementation examples, and algorithms. Learners begin the course by examining what reinforcement learning is and the terms used to formulate reinforcement learning problems. Next, look at the differences between machine learning and reinforcement learning by using supervised and unsupervised learning. Explore the capabilities of reinforcement learning, by looking at use cases and implementation examples. Then learners will examine reinforcement learning workflow and reinforcement learning terms; reinforcement learning algorithms and their features; and the Markov Decision Process, its variants, and the steps involved in the algorithm. Take a look at the Markov Reward Process, focusing on value functions for implementing the Markov Reward Process, and also the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it. The concluding exercise involves recalling reinforcement learning terms, describing implementation approaches, and listing the Markov Decision Process algorithms.
11 videos |
1h 12m
Assessment
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Advanced Reinforcement Learning: Implementation
In this 11-video course, learners can examine the role of reward and discount factors in reinforcement learning, as well as the multi-armed bandit problem and approaches to solving it for machine learning. You will begin by learning how to install the Markov Decision Policy (MDP) toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. Next, examine the role of reward and discount factors in reinforcement learning, and the multi-armed bandit problem and solutions. Learn about dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation. Then learners will explore reinforcement learning agent components and applications; work with reinforcement learning agents using Keras and OpenAI Gym; describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI; and implement deep Q-learning with Keras. Finally, observe how to train deep neural networks (DNN) with reinforcement learning for time series forecasting. In the closing exercise, you will recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning by using Keras and OpenAI Gym.
11 videos |
1h 34m
Assessment
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Applied Deep Learning: Unsupervised Data
This 11-video course explores the concept of deep learning and implementation of deep learning-based frameworks for natural language processing (NLP) and audio data analysis. Discover the architectures of recurrent neural network (RNN) that can be used in modeling NLP, and the challenges of unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature machine learning. First, examine the prominent statistical classification models and compare generative classifiers with discriminative classifiers; then recall different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model. Learn about setting up and working with PixelCNN; explore differences between multilayer perception (MLP), convolutional neural network (CNN), and RNN. Explore the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. Finally, take a look at encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. The concluding exercise involves recalling RNN architecture that can be used in modeling NLP, variants of ResNet, and setting up PixelCNN.
11 videos |
1h 27m
Assessment
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Applied Deep Learning: Generative Adversarial Networks and Q-Learning
Learners will explore variations of generative adversarial network (GAN) and the challenges associated with its models, as well as the concept of deep reinforcement learning, its application for machine learning, and how it differs from deep learning, in this 11-video course. Begin by implementing autoencoders with Keras and Python; implement GAN and the role of Generator and Discriminator; and implement GAN Discriminator and Generator with Python and Keras and build Discriminator for training models. Discover the challenges of working with GAN models and explore the concept of deep reinforcement learning and its application in the areas of robotics, finance, and health care. Compare deep reinforcement learning with deep learning, and examine challenges associated with their implementations. Learn about the basic concepts of reinforcement learning, as well as the concept of deep Q-learning and implementing deep Q-learning. Then implement deep Q-learning in Python by using Keras and OpenAI Gym. The concluding exercise involves recalling variations of GAN, implementing GAN Discriminator and Generator using Python, and implementing deep Q-learning in Python by using Keras and OpenAI Gym.
11 videos |
44m
Assessment
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Enterprise Architecture: Architectural Principles & Patterns
In this 18-video course, learners can explore software architecture concepts, including the view model, consumer-driven contracts, architectural patterns, and architectural styles and solution patterns used to manage common machine learning issues. Begin by examining software architecture and the benefits it provides, and then the principles that should be followed when designing architecture for applications. You will discover the 4+1 view model and associated views, and learn to recognize software architectures, and the principles of developing enterprise architecture. Recall architectural principles for business, data, and technology, and the fundamental principles guiding service-oriented architecture (SOA) and use of the SOA maturity model. Next, explore serverless architecture; Backend-as-a-Service; the features of evolutionary architecture; and learn to recognize benefits of documenting architecture. Examine the structure of a software project team; the concept and characteristics of consumer-driven contracts; the dimensions of architecture that should be coupled to provide maximize benefit with minimal overheads and costs; and activities and tasks that software architects perform. Finally, take a look at architectural patterns and styles that can be adopted to eliminate common problems.
18 videos |
1h 34m
Assessment
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Enterprise Architecture: Design Architecture for Machine Learning Applications
Explore software architectures used to model machine learning (ML) applications in production, as well as the building blocks of ML reference architecture, in this 11-video course. Examine the pitfalls and building approaches for evolutionary architectures, Fitness function categories, architectural planning guidelines for ML projects, and how to set up complete ML solutions. Learners will begin by studying the basic architecture required to execute ML in enterprises, and will also take a look at software architecture and its features that can be used to model ML apps in production. Next, learn how to set up model ML apps; examine ML reference architecture and the associated building blocks; and view the approaches for building evolvable architectures and migration. Recognize the critical pitfalls of evolutionary architecture and antipatterns of technical architecture and change. Finally, observe how to set up complete ML solutions and explore the Fitness function and its associated categories. Conclude the course with an exercise on architectural planning guidelines for ML projects, with a focus on model refinement, testing, and evaluating production readiness.
11 videos |
59m
Assessment
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ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
This 13-video course explores various standards and frameworks that can be adopted to build, deploy, and implement machine learning (ML) models and workflows. Begin with a look at the critical challenges that may be encountered when implementing ML. Examine essential stages of ML processes that need to be adopted by enterprises. Then explore the development lifecycle exclusively used to build productive ML models, and the essential phases of ML workflows. Recall the critical processes involved in training ML models; observe the various on-premises and cloud-based platforms for ML; and view the approaches that can be adopted to model and process data for productive ML deployments. Next, see how to set up a ML environment by using H2O clusters; recall various data stores and data management frameworks used as a data layer for ML environments; and specify the processes involved in implementing ML pipelines and using visualizations to generate insights. Finally, set up and work with Git to facilitate team-driven development and coordination across the enterprise. The concluding exercise concerns ML training processes.
13 videos |
1h 4m
Assessment
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ML/DL in the Enterprise: Pipelines & Infrastructure
Learners will discover the infrastructure, frameworks, and tools that can be used to build data pipelines and visualization for machine learning (ML) in this 10-video course exploring end-to-end approaches for building and deploying ML applications. You will begin with a look at approaches to identifying the right infrastructure for data and ML, and building data pipelines for ML deployments. Examine the iterative process in building ML models with Machine Learning Studio; implement machine learning visualization, and classify frameworks and tools for ML. Next, observe how to build generalized low-rank models by using H2O and integrate them into a data science pipeline to make better predictions. Explore the role of model metadata in applying governance in ML, and also ML risk mitigation-recognizing how ML risk analysis and management approaches can be used to mitigate risks effectively. In the exercise you will recall learning build and deployment frameworks, use Python to implement visualization for ML, and build a simple ML model by using Microsoft Azure Machine Learning Studio.
10 videos |
53m
Assessment
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ML & Dimensionality Reduction: Performing Principal Component Analysis
Principal component analysis (PCA) is a must-know pre-processing technique for anyone working with machine learning (ML). Used to process data fed into ML models, PCA is useful in many scenarios, such as exploratory data analysis, dimensionality reduction, and latent feature extraction. Use this course to learn the basic intuition behind principal component analysis along with how to use PCA. Start by visualizing how principal components work. Then, examine how they can be computed mathematically using the eigenvectors and eigenvalues of the covariance matrix of the data. As you advance, manually compute principal components, view the re-oriented data, and compare this result with the principal components computed. Lastly, use PCA for dimensionality reduction to train a classification model. When you're done, you'll have the skills and knowledge to use PCA to build more robust machine learning models.
11 videos |
1h 15m
Assessment
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COURSES INCLUDED
Low-code ML with KNIME: Building Regression Models
Regression analysis is used to predict continuous data values. The KNIME Analytics Platform allows you to load, explore, pre-process, and use data to train regression models with little to no code. Through this course, learn how to train and evaluate regression models in KNIME. Explore how regression models work and use KNIME nodes to build a workflow to load and comprehend data. Next, discover how to compute correlations and use bar charts, box plots, scatter plots, and pivot tables. Finally, learn how to pre-process flight prediction data using one-hot and label encoding, partition data, and train regression models. After course completion, you'll be able to build a complete workflow in KNIME for regression analysis.
15 videos |
1h 35m
Assessment
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Low-code ML with KNIME: Building Classification Models
Classification models are used to categorize data into a fixed number of discrete classes or categories. The KNIME Analytics Platform allows you to load, explore, pre-process, and use your data to train classification models with little to no code. In this course, explore classification models and the metrics used to evaluate their performance. Next, construct a KNIME workflow to load and view the data for a classification model. You will clean data, impute missing values, and cap and floor outlier values in a range. Then you will identify and filter correlated variables and you will convert categorical data to numeric values and express numeric variables. Finally, train several different classification models on the training data, evaluate them using the test data, and select the best model using hyperparameter tuning. Upon completing this course, you will have the skills and knowledge to train, clean, and process your data and to use that data to train classification models and perform hyperparameter tuning.
16 videos |
2h 5m
Assessment
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Low-code ML with KNIME: Building Clustering Models
Clustering is an unsupervised learning technique that finds logical groupings or clusters in your data, for example, identifying what social network users have the same interests and background. In this course, explore how clustering models seek to find logical groupings in your data. Next, construct a KNIME workflow to load and explore data for a clustering model. Then, fill in missing values using different imputation techniques, identify highly correlated variables, and deal with outliers. Fit a k-means clustering model on your data, identify clusters, and use scatter plots to visualize the clusters in your data. Finally, perform dimensionality reduction using principal component analysis (PCA) and use the silhouette score to evaluate the number of clusters that gives you the best clustering for your data. Upon course completion, you will be able to fit and evaluate clustering models on your data and visualize clusters using 2-D and 3-D visualizations.
10 videos |
1h 3m
Assessment
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Low-code ML with KNIME: Performing Time Series & Market Basket Analysis
Organizations use time series analysis and market basket analysis to understand patterns over time. Time series analysis uses data collected over regular intervals to analyze how the variable changes over time, while market basket analysis is an application of association rule learning that tries to learn what items occur together frequently in the same transaction. In this course, discover how time series analysis works and how time series models like the autoregressive integrated moving average (ARIMA) model can help you forecast future values of time-varying data using historical values. Next, visualize time series data using moving averages and time series decomposition and fit an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a bakery and perform association rule learning on this data to figure out what items are frequently bought together. Upon course completion, you will be able to confidently use KNIME for time series analysis and market basket analysis.
12 videos |
1h 25m
Assessment
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COURSES INCLUDED
No-code ML with RapidMiner: Performing Regression Analysis
Regression is used in the real world to predict things like stock prices, car mileage, or insurance premiums. RapidMiner studio offers an easy-to-use visual designer that allows you to construct a regression workflow with little to no code. In this course, explore regression models and the R-squared metric used to evaluate regression models. Next, use RapidMiner to retrieve data and use it for modeling. Then, automate data preparation with Turbo Prep, automate the training of multiple regression models using Auto Model, and compare these models using RapidMiner. Build a workflow to train regression models by using operators for data cleaning, imputing missing values, one-hot encoding, and partitioning your data. Finally, train multiple models for regression analysis and compare their performance and perform hyperparameter tuning to get the best model design for your use case. When you are finished with this course, you will be able to build a complete workflow in RapidMiner for regression analysis and improve your model using hyperparameter tuning.
16 videos |
1h 58m
Assessment
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No-code ML with RapidMiner: Building & Using Classification Models
Classification models are used in the real world to predict whether to buy, sell, or hold a particular stock or to identify objects in images. RapidMiner studio supports features such as Turbo Prep and Auto Model that completely automate data processing and model building. In this course, discover how classification models can be used to categorize input records and how metrics such as accuracy, precision, and recall can be used to evaluate those classification models. Next, create a process to retrieve, summarize, and visualize data using operators. Finally, configure your own workflow for classification, and train and compare a logistic regression model and a random forest model. You will choose the best-performing model for local deployment on your machine and see how you can use deployed models for predictions. Once you have completed this course you will have the skills to train, clean, and process data in order to train classification models and deploy your model locally.
11 videos |
1h 20m
Assessment
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No-code ML with RapidMiner: Performing Clustering Analysis
Clustering models work with unlabeled data, finding logical groupings in data, and are often used for social media ad targeting and document discovery. In this course, explore the clustering unsupervised learning technique. Next, retrieve data from the repository into your process and use Turbo Prep to clean and preprocess the data for clustering analysis. Then use Auto Model to train k-means and x-means clustering models on your data and evaluate and visualize the models created. Finally, create your own analytics process for k-means clustering, evaluate your model using the Davies-Bouldin score, use principal component analysis (PCA) to better visualize the clusters found in your data, and determine the ideal number of clusters by using hyperparameter tuning. When you are finished with this course, you will be able to fit and evaluate clustering models on your data and visualize clusters with data points plotted using principal components.
9 videos |
1h 1m
Assessment
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No-code ML with RapidMiner: Time-series Forecasting & Market Basket Analysis
Time series forecasting uses data collected over periodic intervals to analyze how the variable changes over time. Time series analysis is often used for forecasting problems such as demand forecasting and revenue forecasting. In this course, discover how time series analysis works and how time series models such as the autoregressive integrated moving average (ARIMA) model can help forecast future values of time-varying data using historical values. Next, visualize and explore time series data using windowing, differencing, moving averages, and time series decomposition. Then fit a function, a seasonal component model, and an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a grocery store and perform association rule learning on this data to figure out what items are frequently bought together. When you are finished with this course you will have the skills to use RapidMiner for time-series forecasting and market basket analysis.
16 videos |
1h 43m
Assessment
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Machine Learning with BigQuery ML: Building Classification Models
Predictive models that output discrete classes or categories are classification models. Classification is widely used in the real world for use cases such as sentiment analysis of text and identifying objects in images. In this course, you will review how classification models can be used to categorize or classify input records. You will learn how metrics such as accuracy, precision, and recall can be used to evaluate classification models and the conditions under which you would choose to use precision and recall over accuracy for model evaluation. Next, you will use the BigQuery command-line tool bq to create a BigQuery dataset and table and load data into that table. You will see how you can run queries and explore your data, all using the command line. You will use Looker Studio for data visualization and DataPrep to clean and prepare your classification data. Finally, you will train a binary classification model and a multi-class classification model. You will improve the model's performance by balancing the records in the different categories and by using hyperparameter tuning to find the best model for your data.
13 videos |
1h 47m
Assessment
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Machine Learning with BigQuery ML: Building Unsupervised Models
Unsupervised techniques such as clustering and recommendation systems can discover patterns in unlabeled data. These models extract structure in the x-variables or features present in the data. In this course, you will work with two unsupervised learning methods, clustering and recommendation systems. You will explore how clustering algorithms use only the x-variables or features in your data to group data into logical clusters. Then you will discover the basic concepts behind recommendation systems, which take in past user interactions with products and use that to recommend new products to users. Next, you will train a clustering model using k-means clustering on your data and evaluate how the clusters differ. You will use hyperparameter tuning to find the best number of clusters on your dataset. Finally, you will train a recommendations engine using collaborative filtering and use that to make movie recommendations to users based on their past preferences and the preferences of other users.
13 videos |
1h 41m
Assessment
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Machine Learning with BigQuery ML: Training Time Series Forecasting Models
Time series forecasting uses data collected over periodic intervals to understand and analyze how the variable changes over time. Time series analysis is used for forecasting problems, such as demand forecasting and revenue forecasting. The auto-regressive integrated moving average (ARIMA) model is widely used for time series forecasting. In this course, you will see how time series analysis works and how models such as the ARIMA model can help you forecast future values of time-varying data using historical values. You will also learn the differences between stationary and non-stationary time series data. Next, you will load and explore your time series data for store revenue prediction into BigQuery and visualize and explore this data using Looker Studio. Finally, you will use an ARIMA model to make revenue forecasts. You will see how BigQuery ML trains multiple ARIMA models to find the best auto-regressive, differencing, and moving average parameters for your data. You will also perform multiple time-series analysis by forecasting store revenue by region.
8 videos |
57m
Assessment
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COURSES INCLUDED
MLOps with MLflow: Creating & Tracking ML Models
With MLflow's tracking capabilities, you can easily log and monitor experiments, keeping track of various model runs, hyperparameters, and performance metrics. In this course, you will dive hands-on into implementing the ML workflow, including data preprocessing and visualization. You will focus on loading, cleaning, and analyzing data for machine learning. You will visualize data with box plots, heatmaps, and other plots and use the Pandas profiling tool to get a comprehensive view of your data. Next, you will dive deeper into MLflow Tracking and explore features that enhance experimentation and model development. You will create MLflow experiments to group runs and manage them effectively. You will compare multiple models and visualize performance using the MLflow user interface (UI), which can aid in model selection for further optimization and deployment. Finally, you will explore the capabilities of MLflow autologging to automatically record experiment metrics and artifacts and streamline the tracking process.
15 videos |
1h 45m
Assessment
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MLOps with MLflow: Registering & Deploying ML Models
The MLflow Model Registry enables easy registration and deployment of machine learning (ML) models for future use, either locally or in the cloud. It streamlines model management, facilitating collaboration among team members during model development and deployment. In this course, you will create classification models using the regular ML workflow. You'll see that visualizing and cleaning data, running experiments, and analyzing model performance using SHapley Additive exPlanations (SHAP) will provide valuable insights for decision-making. You'll also discover how programmatic comparison will aid in selecting the best-performing model. Next, you'll explore the powerful MLflow Models feature, enabling efficient model versioning and management. You'll learn how to modify registered model versions, work with different versions of the same model, and serve models to Representational State Transfer (REST) endpoints. Finally, you'll explore integrating MLflow with Azure Machine Learning, leveraging the cloud's power for model development.
15 videos |
1h 57m
Assessment
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MLOps with MLflow: Hyperparameter Tuning ML Models
Hyperparameter tuning, an essential step to improve model performance, involves modifying a model's parameters to find the best combination for optimal results. The integration of MLflow with Databricks unlocks a powerful combination that enhances the machine learning (ML) workflow. First, you will explore the collaborative potential between MLflow and Databricks for machine learning projects. You will learn to create an Azure Databricks workspace and run MLflow models using notebooks in Databricks, establishing a robust foundation for model development in a scalable environment. Additionally, you will set up Databricks File System (DBFS) as a source of model input files. Next, you will implement hyperparameter tuning using MLflow and its integration with the hyperopt library. You will define the objective function, search space, and algorithm to optimize model performance. Through systematic tracking and comparison of hyperparameter configurations with MLflow, you will find the best-performing model setups. Finally, you will integrate SQLite with MLflow, allowing efficient management and storage of experiment-run data. You will create a regression model using scikit-learn and statsmodels, comparing the processes for the two.
12 videos |
1h 37m
Assessment
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MLOps with MLflow: Creating Time-series Models & Evaluating Models
MLflow integrates with Prophet, a powerful time-series model that considers seasonal effects. MLflow provides a variety of model evaluation capabilities, empowering you to thoroughly assess and analyze model performance. First, you will use Prophet in combination with MLflow for time-series forecasting. Integrating Prophet with MLflow's tracking capabilities, you will seamlessly manage and evaluate your time-series models. Running the Prophet model and viewing metrics will allow you to assess its forecasting performance. Cross-validation will enhance the evaluation process, ensuring reliability across different temporal windows. Then, you will use MLflow to evaluate machine learning (ML) models effectively. MLflow's evaluation capabilities, including Lift curves, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curves, precision-recall curves, and beeswarm charts, provide valuable insights into model behavior and performance. Finally, you will use MLflow to configure thresholds for model metrics and only validate those models which meet this threshold.
10 videos |
1h 23m
Assessment
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MLOps with MLflow: Tracking Deep Learning Models
Deep learning models have revolutionized computer vision and natural language processing, enabling powerful image and text-based predictions. You will start with image-based predictions using TensorFlow. You will visualize and clean data to generate datasets ready for machine learning (ML). You will train an image classification model with TensorFlow and track metrics and artifacts using MLflow. You will register the model in MLflow for local deployment and deployment on Azure. Next, you will explore PyTorch Lightning to simplify deep learning model development and training. You will use it for image classification, setting up your model with little effort. You will then train an image classification model with MLflow for tracking, deploy it locally, and expose it for predictions using a REST endpoint. Finally, you will get an overview of large language models (LLMs) like Transformers. You will load a pre-trained Transformers-based sentiment analysis model from Hugging Face and use MLflow to track its performance and artifacts.
10 videos |
1h 31m
Assessment
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MLOps with MLflow: Using MLflow Projects & Recipes
MLflow Projects enable you to package machine learning code, data, and environment specifications for reproducibility and easy sharing. Registering projects in MLflow simplifies version control and enhances collaboration within data science teams. MLflow Recipes, on the other hand, automate and standardize machine learning tasks with pre-defined templates and configurations, promoting consistency and repeatability while allowing customization for specific applications. With recipes and projects combined, MLflow becomes a powerful tool for impactful and consistent results, streamlining data science workflows. You will start this course by learning how MLflow Projects enable you to package, share, and reproduce machine learning code. Next, you will learn about MLflow Recipes that automate machine learning tasks in reproducible environments. You will explore the MLflow Regression Template, customize its files for model training, and run the recipe to view the model's performance. Finally, you will explore running a classification recipe in Databricks and modifying YAML and code files for configuration.
17 videos |
2h 8m
Assessment
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MLOps with Data Version Control: Tracking & Serving Models with DVC & MLEM
Data Version Control (DVC) enables model tracking by versioning machine learning (ML) models alongside their associated data and code, allowing seamless reproducibility of model training and evaluation across different environments and collaborators. MLEM is a tool that easily packages, deploys, and serves ML models. In this course, you will compare ML model performance using DVC. You will create multiple churn-prediction classification models employing various algorithms, including logistic regression, random forests, and XGBoost and you will track metrics, parameters, and artifacts. Then you will leverage the Iterative Studio interface to visually contrast models' metrics and performance graphs and perform comparisons using the command line. Next, you will unlock the potential of hyperparameter tuning with the Optuna framework. You will tune your ML model, compare the outcomes of hyperparameter-tuned models, and select the optimal model for deployment. Finally, you will codify and move your ML model through REST endpoints and Docker-hosted container deployment, solidifying your understanding of serving MLEM models for predictions. This course will equip you with comprehensive knowledge of codifying and serving ML models.
15 videos |
1h 53m
Assessment
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MLOps with Data Version Control: Tracking & Logging Deep Learning Models
Data Version Control (DVC) offers robust support for deep learning models by effectively managing large model files and their dependencies, allowing versioned tracking of complex architectures. This ensures reproducibility in training, evaluation, and deployment pipelines, even in deep learning projects. In this course, you will discover how to track deep learning models through DVC. Using PyTorch Lightning, you will construct a convolutional neural network (CNN) for image classification. Then you will use DVCLive to log and visualize sample images and use the DVCLiveLogger to monitor model metrics in real time via Iterative Studio. Next, you will undertake deep learning model training with TensorFlow. You will set up a CNN for image classification and train your model while leveraging DVCLive to record and display training-related metrics. Finally, you will use the DVCLiveCallback to dynamically visualize metrics during training. This course will equip you with the expertise to effectively build and track deep learning models within DVC's ecosystem.
12 videos |
1h 30m
Assessment
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COURSES INCLUDED
Implementing AI With Amazon ML
Amazon offers AI developers a wide variety of tools and frameworks including Amazon Web Services (AWS) and the Amazon Machine Learning (ML) framework. By integrating complex machine and deep learning development with the extensive computing capabilities of Amazon, Amazon ML allows AI developers to adopt big data AI services. With many companies actively using AWS and Amazon ML, a basic knowledge of this framework is beneficial. In this course, you'll learn how to use Amazon ML together with AWS, to work with big data, and to create machine and deep learning models. You'll also examine the basics of automated model deployment with Amazon SageMaker. Next, you'll explore how to use Amazon ML for image and video analysis, text-to-speech translation, and text analytics. Finally, you'll implement a system to analyze movie review sentiment using the Amazon ML framework.
15 videos |
38m
Assessment
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Extending Amazon Machine Learning
The Amazon Machine Learning framework allows you to quickly deploy machine learning models using Amazon Web Services, automate model deployment and maintenance, and configure other Amazon tools to work in synchronicity. AI practitioners should consider the benefits and best practices of working with Amazon ML and other Amazon services in their AI development projects. In this course, you'll explore advanced techniques for working with the Amazon ML framework. You'll examine the significant differences between Amazon ML and other frameworks. You'll recognize the advantages of using the Amazon ML platform for certain projects and identify the Amazon ML workflow. Finally, you'll complete a project developing and training an AI model using the Amazon ML framework, and troubleshoot typical problems that come up during model training and evaluation.
15 videos |
1h 1m
Assessment
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COURSES INCLUDED
MLOps with Data Version Control: Creating & Using DVC Pipelines
Data Version Control (DVC) pipelines empower data practitioners to define, automate, and version complex data processing workflows. By streamlining end-to-end processes, pipelines enhance collaboration, maintain data lineage, and enable efficient experimentation and deployment in data-centric projects. In this course, you will discover the intricacies of machine learning (ML) pipelines within DVC. You will set up a pipeline with data cleaning, training, and evaluation stages and run these stages using the dvc repro command. Then you will use DVC to track the status of the pipeline with the help of the dvc.lock file. Next, you will run and track a DVC pipeline as an experiment using DVCLive and view metrics and artifacts of your pipeline in the Iterative Studio user interface. Finally, you will queue DVC experiments so they can be run later, either in parallel or sequentially. This course gives you an in-depth understanding of DVC pipelines, equipping you to seamlessly orchestrate and manage your ML workloads.
12 videos |
1h 21m
Assessment
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MLOps with Data Version Control: CI/CD Using Continuous Machine Learning
Continuous integration and continuous deployment (CI/CD) are crucial in machine learning operations (MLOps) as they automate the integration of ML models into software development. Continuous machine learning (CML) refers to an ML model's ability to learn continuously from a stream of data. In this course, you will build a complete Data Version Control (DVC) machine learning pipeline in preparation for continuous machine learning. You will modularize your machine learning workflow using DVC pipelines, configure DVC remote storage on Google Drive, and set up authentication for DVC to access Google Drive. Next, you will configure CI/CD through CML and use the open-source CML framework to implement CI/CD within your machine learning project. Finally, you will see how for every git push to your remote repository, a CI/CD pipeline will execute your experiment and generate a CML report with model metrics for every GitHub commit. At the end of this course, you will be able to use DVC's integration with CML to build CI/CD pipelines.
9 videos |
1h 2m
Assessment
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EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Machine Learning: Algorithms and ApplicationsExplaining the concepts of machine learning algorithms, this practical book describes the application areas of each algorithm discussed, and uses simple, practical examples to help readers understand each algorithm.
2h 6m
By Eihab Bashier Mohammed Bashier, Mohssen Mohammed, Muhammad Badruddin Khan
Book
An Introduction to Machine LearningPresenting basic ideas of machine learning in a way that is easy to understand, this book provides hands-on practical advice, uses simple examples, and motivates students with discussions of interesting applications.
5h 52m
By Miroslav Kubat
Book
Introduction to Machine Learning, Third EditionA comprehensive textbook on the subject, this book covers a broad array of topics including supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.
9h 22m
By Ethem Alpaydin
Book
Machine Learning: The New AIA concise overview of machine learning, this book offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.
2h 36m
By Ethem Alpaydin
Book
Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent SystemsUsing real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner.
9h 59m
By Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Book
Building Intelligent Systems: A Guide to Machine Learning EngineeringTeaching you about leveraging machine learning in practice, this book covers everything you need to produce a fully functioning Intelligent System, one that leverages machine learning and data from user interactions to improve over time and achieve success.
4h 58m
By Geoff Hulten
Book
Machine Learning: A Concise IntroductionIncluding many thoughtful exercises as an integral part of the text, with an appendix of selected solutions, this practical resource offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning.
5h 2m
By Steven W. Knox
Book
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second EditionProviding simple yet insightful quantitative techniques, this book contains essays offering detailed background, discussion, and a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
7h 54m
By Bruce Ratner
Book
Real-World Machine LearningWithout overdosing you on academic theory and complex mathematics, this book introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
4h 13m
By Henrik Brink, Joseph W. Richards, Mark Fetherolf
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BOOKS INCLUDED
Book
Deep Learning for Natural Language Processing: Creating Neural Networks with PythonHelping you discover the concepts of deep learning used for natural language processing (NLP), this book provides full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.
2h 37m
By Palash Goyal, Sumit Pandey
Book
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural NetworksWith complete and complex examples throughout, this book will teach you how to work with advanced topics in deep learning, and will show you strategies to address typical problems encountered when training deep neural networks.
5h 8m
By Umberto Michelucci
Book
Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP ExamplesContaining practical real-world examples of data analytics, this book will provide you with a broad foundation of advanced data analytics concepts and knowledge of the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB.
1h 24m
By Sayan Mukhopadhyay
Book
Deep Learning for DummiesProviding real-world examples and hands-on activities to make learning easier, this approachable text gives you the information you need to take the mystery out of the topic-as well as all of the underlying technologies associated with it.
6h
By John Paul Mueller, Luca Massaron
Book
Deep Learning Pipeline: Building a Deep Learning Model with TensorFlowTeaching you what a pipeline is and how it works so you can build a full application easily and rapidly, this step-by-step book will help you troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models.
5h 49m
By Hisham El-Amir, Mahmoud Hamdy
Book
Artificial Intelligence, Machine Learning and Deep LearningIntroducing advanced beginners to basic machine learning and deep learning concepts and algorithms, this book is intended to be a fast-paced introduction to various "core" features of machine learning and deep learning, with code samples that are included in a university course.
4h
By Oswald Campesato
Book
Deep Learning with Structured DataFilled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems.
4h 7m
By Mark Ryan
Book
Getting started with Deep Learning for Natural Language Processing: Learn how to build NLP applications with Deep LearningThis book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN).
5h
By Sunil Patel
Book
Applied Deep Learning: Design and Implement Your Own Neural Networks to Solve Real-World ProblemsDeep Learning has become increasingly important due to the growing need to process and make sense of vast amounts of data in various fields. If you want to gain a deeper understanding of the techniques and implementations of deep learning, then this book is for you.
5h 13m
By Dr. Neeraj Kumar, Dr. Rajkumar Tekchandani
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BOOKS INCLUDED
Book
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with PythonWith practical business-centric use-cases implemented in Keras, this concise resource provides you with a thorough understanding of deep learning principles and practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
2h 7m
By Jojo Moolayil
Book
Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with TensorFlow and KerasExploring deep learning applications using frameworks such as TensorFlow and Keras, this book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications.
1h 28m
By Navin Kumar Manaswi
Book
Reinforcement Learning: With Open AI, TensorFlow and Keras Using PythonCovering the basics of Reinforcement Learning with the help of the Python programming language, this book touches on several aspects, such as Q learning, MDP, RL with Keras, and OpenAI Gym and OpenAI Environment, and also cover algorithms related to RL.
1h 6m
By Abhishek Nandy, Manisha Biswas
BOOKS INCLUDED
Book
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial IntelligenceHelping you get started with MATLAB for deep learning and AI, this in-depth primer offers examples, case studies and a step-by-step approach while demonstrating how to counter real world problems found in big data, smart bots and more.
1h 47m
By Phil Kim
Book
Machine Learning: Hands-on for Developers and Technical ProfessionalsOffering clear guidance for the non-mathematician, this accessible, comprehensive guide provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals.
5h 7m
By Jason Bell
Book
Feature Engineering for Machine Learning and Data AnalyticsPresenting key concepts, methods, examples, and applications, this book provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.
7h 27m
By Guozhu Dong, Huan Liu (eds)
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Reinforcement Learning: With Open AI, TensorFlow and Keras Using PythonCovering the basics of Reinforcement Learning with the help of the Python programming language, this book touches on several aspects, such as Q learning, MDP, RL with Keras, and OpenAI Gym and OpenAI Environment, and also cover algorithms related to RL.
1h 6m
By Abhishek Nandy, Manisha Biswas
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Book
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural NetworksWith complete and complex examples throughout, this book will teach you how to work with advanced topics in deep learning, and will show you strategies to address typical problems encountered when training deep neural networks.
5h 8m
By Umberto Michelucci
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Deep Learning for Natural Language Processing: Creating Neural Networks with PythonHelping you discover the concepts of deep learning used for natural language processing (NLP), this book provides full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.
2h 37m
By Palash Goyal, Sumit Pandey
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Advanced Applied Deep Learning: Convolutional Neural Networks and Object DetectionDevelop and optimize deep learning models with advanced architectures, this book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks.
2h 49m
By Umberto Michelucci
Book
Grokking Deep LearningTeaching you to build deep learning neural networks from scratch, this book, written in an engaging style, shows you the science under the hood, so you grok for yourself every detail of training neural networks.
3h 57m
By Andrew W. Trask
Book
Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and ClassificationIntroducing the fundamental concepts of convolutional neural networks (ConvNets), this self-contained guide offers practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification.
5h 22m
By Elnaz Jahani Heravi, Hamed Habibi Aghdam
Book
Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and KivyExplaining the basic concepts of deep learning (DL) using numerical examples, this book targets those of tomorrow's data scientists who would like to start understanding the basic concepts of DL for computer vision.
4h 56m
By Ahmed Fawzy Gad
Book
Artificial Neural Networks with Java: Tools for Building Neural Network ApplicationsShowing you how to use Java to develop neural network applications, this practical book uses a step-by-step approach including plenty of examples, diagrams, and screenshots to facilitate your learning experience.
3h 59m
By Igor Livshin
Book
Principles of Artificial Neural Networks, 3rd EditionCovering the basic theory and architecture of the major artificial neural networks, this unique book presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition.
4h 5m
By Daniel Graupe
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Book
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and FinanceGain practical skills in machine learning for finance, healthcare, and retail.
6h 5m
By Puneet Mathur
Book
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with PythonWith practical business-centric use-cases implemented in Keras, this concise resource provides you with a thorough understanding of deep learning principles and practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
2h 7m
By Jojo Moolayil
Book
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and RBridging the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better, this book will give you the confidence and skills needed when developing all the major machine learning models.
3h 16m
By V Kishore Ayyadevara
Book
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural LanguageLearn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation.
1h 41m
By Taweh Beysolow II
Book
Python for Data Mining Quick Syntax ReferenceCovering each concept concisely with many illustrative examples, this book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.
1h 52m
By Valentina Porcu
Book
PyTorch Recipes: A Problem-Solution ApproachFor readers wanting to dive straight into programming PyTorch, this book adopts a problem-solution approach to PyTorch programming, includes deep learning algorithms with PyTorch and covers natural language processing and text processing.
1h 7m
By Pradeepta Mishra
Book
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python, Second EditionYour practical guide to moving from novice to master in machine learning with Python 3 in six steps, this book covers fundamental to advanced topics gradually helping beginners become worthy practitioners.
4h 20m
By Manohar Swamynathan
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BOOKS INCLUDED
Book
Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web ServicesHelping you understand the importance of data and how to organize it for use within your machine learning (ML) project, this book includes case study examples and projects that you can take away as templates for re-use and exploration for your own ML programming projects with Java.
4h 13m
By Mark Wickham
Book
Machine Learning: A Concise IntroductionIncluding many thoughtful exercises as an integral part of the text, with an appendix of selected solutions, this practical resource offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning.
5h 2m
By Steven W. Knox
Book
Machine Learning: The New AIA concise overview of machine learning, this book offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.
2h 36m
By Ethem Alpaydin
Book
Machine Learning: Algorithms and ApplicationsExplaining the concepts of machine learning algorithms, this practical book describes the application areas of each algorithm discussed, and uses simple, practical examples to help readers understand each algorithm.
2h 6m
By Eihab Bashier Mohammed Bashier, Mohssen Mohammed, Muhammad Badruddin Khan
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BOOKS INCLUDED
Book
The Bayesian Way: Introductory Statistics for Economists and EngineersFilled with helpful illustrations, this comprehensive text offers a basic introduction to statistics that emphasizes the Bayesian approach and is designed for use by those studying professional disciplines like engineering and economics.
8h 38m
By Svein Olav Nyberg
Book
Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical EngineeringEssential reading for researchers, practitioners and students in mechanical and aerospace engineering, this book covers probabilistic finite element model updating, achieved using Bayesian statistics.
4h 14m
By Ilyes Boulkaibet, Sondipon Adhikari, Tshilidzi Marwala
Book
Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, Second EditionWith an expansion of case studies that detail Bayesian solutions for a variety of applications, this comprehensive resource presents the Bayesian approach to statistical signal processing for a variety of useful model sets.
8h 19m
By James V. Candy
Book
Machine Learning: A Bayesian and Optimization PerspectiveIncluding all major classical techniques, the latest trends, case studies and MATLAB code, this tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches.
20h 10m
By Sergios Theodoridis
Book
Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's GuidebookEmploying a modern computational approach known as Markov chain Monte Carlo (MCMC), this book provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.
3h 43m
By Curtis Smith, Dana Kelly
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BOOKS INCLUDED
Book
Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWSSuccessfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment with this thorough guide.
2h 25m
By Himanshu Singh
BOOKS INCLUDED
Book
Machine Learning with TensorFlowUsing a down-to-earth teaching style, this book gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.
3h 34m
By Nishant Shukla
Book
TensorFlow for DummiesProviding a friendly, easy-to-follow book on TensorFlow, this thorough resource tames this sometimes intimidating technology and explains, in simple steps, how to write TensorFlow applications.
4h 30m
By Matthew Scarpino
Book
Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in PythonProviding practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions, this book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures with ease.
5h 48m
By Santanu Pattanayak
BOOKS INCLUDED
Book
Machine Learning with Python: An Approach to Applied Machine LearningProviding code examples in python, this book introduces the concepts of machine learning with mathematical explanations and programming fundamentals.
2h 30m
By Abhishek Vijayvargia
Book
Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP ExamplesContaining practical real-world examples of data analytics, this book will provide you with a broad foundation of advanced data analytics concepts and knowledge of the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB.
1h 24m
By Sayan Mukhopadhyay
Book
Feature Engineering for Machine Learning and Data AnalyticsPresenting key concepts, methods, examples, and applications, this book provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.
7h 27m
By Guozhu Dong, Huan Liu (eds)
Book
Deep Learning Pipeline: Building a Deep Learning Model with TensorFlowTeaching you what a pipeline is and how it works so you can build a full application easily and rapidly, this step-by-step book will help you troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models.
5h 49m
By Hisham El-Amir, Mahmoud Hamdy
Book
Artificial Intelligence, Machine Learning and Deep LearningIntroducing advanced beginners to basic machine learning and deep learning concepts and algorithms, this book is intended to be a fast-paced introduction to various "core" features of machine learning and deep learning, with code samples that are included in a university course.
4h
By Oswald Campesato
Book
Deep LearningAn accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.
3h 11m
By John D. Kelleher
Book
Advanced Applied Deep Learning: Convolutional Neural Networks and Object DetectionDevelop and optimize deep learning models with advanced architectures, this book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks.
2h 49m
By Umberto Michelucci
Book
Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for BeginnersUsing a systematic approach, this resource is a one-stop shop that takes the beginner on a journey to understanding the theoretical foundations and the practical steps for leveraging machine learning and deep learning techniques on problems of interest.
5h 20m
By Ekaba Bisong
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BOOKS INCLUDED
Book
Deep Learning on Microcontrollers: Learn How to Develop Embedded AI Applications Using TinyMLTinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you.
3h 29m
By Atul Krishna Gupta, Dr. Siva Prasad Nandyala
Book
Mastering Classification Algorithms for Machine Learning: Learn How to Apply Classification Algorithms for Effective Machine Learning SolutionsClassification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you.
4h 4m
By Partha Majumdar
Book
Machine Learning in Production: Master The Art of Delivering Robust Machine Learning Solutions with MLOps‘Machine Learning in Production' is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production.
3h 56m
By Suhas Pote
Book
Hands-on TinyML: Harness The Power of Machine Learning on The Edge DevicesTinyML is an innovative technology that empowers small and resource-constrained edge devices with the capabilities of machine learning. If you're interested in deploying machine learning models directly on microcontrollers, single board computers, or mobile phones without relying on continuous cloud connectivity, this book is an ideal resource for you.
4h 9m
By Rohan Banerjee
Book
Machine Learning Techniques for VLSI Chip DesignThis cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design.
3h 3m
By Abhishek Kumar, K. Srinivasa Rao, Suman Lata Tripathi
Book
Evolutionary Deep Learning: Genetic Algorithms and Neural NetworksDiscover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning's common pitfalls and deliver adaptable model upgrades without constant manual adjustment.
5h 41m
By Micheal Lanham
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BOOKS INCLUDED
Book
Predictive Analytics and Data Mining: Concepts and Practice with RapidMinerWhether you are brand new to data mining or working on your tenth project, this easy-to-understand book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.
6h 56m
By Bala Deshpande, Vijay Kotu
SKILL BENCHMARKS INCLUDED
AI Landscape Literacy (Beginner Level)
The AI Landscape Literacy (Beginner Level) benchmark measures your ability to recall and recognize the fundamentals of artificial intelligence (AI) and machine learning (ML). You will be evaluated on your knowledge of how algorithms learn and perform common tasks like classification and clustering and the importance of deep learning models. A learner who scores high on this benchmark demonstrates that they have the basic foundational knowledge of AI.
18m
| 18 questions
SKILL BENCHMARKS INCLUDED
Applied Machine Learning with Python Competency (Intermediate Level)
The Applied Machine Learning with Python Competency benchmark will measure your ability to identify and apply machine learning algorithms to build learning systems. A learner who scores high on this benchmark demonstrates that they have the machine learning skills necessary to model data and build learning systems.
23m
| 23 questions
Applied Machine Learning with Python Literacy (Beginner Level)
The Applied Machine Learning with Python Literacy benchmark will measure your ability to identify machine learning algorithms and models, and principles behind building these systems. A learner who scores high on this benchmark demonstrates that they have a basic understanding of machine learning fundamentals.
19m
| 19 questions
ML & DL Algorithms Competency (Intermediate Level)
The ML & DL Algoritms Competency (Intermediate Level) benchmark assesses your recognition of core ML & DL Algoritms You will be evaluated on your skills in recognizing high-level elements of ML & DL Algoritms. Learners who score high on this benchmark demonstrate that they have a solid understanding of intermediate-level ML & DL Algoritms.
20m
| 10 questions
SKILL BENCHMARKS INCLUDED
Low-code Machine Learning with KNIME Awareness (Entry Level)
The Low-code Machine Learning with KNIME Awareness (Entry Level) benchmark measures your ability to identify the features and capabilities of the KNIME analytics platform. You will be evaluated on your knowledge of KNIME basics, machine learning, supervised and unsupervised learning techniques, and regression models. A learner who scores high on this benchmark demonstrates that they have a good awareness of the KNIME analytics platform.
8m
| 8 questions
SKILL BENCHMARKS INCLUDED
No-code Machine Learning with RapidMiner Awareness (Entry Level)
The No-code Machine Learning with RapidMiner Awareness (Entry Level) benchmark measures your ability to identify the features and capabilities of RapidMiner for data analytics. You will be evaluated on your knowledge of how to load and summarize data in RapidMiner and perform regression. A learner who scores high on this benchmark demonstrates that they have a basic understanding of how to use the RapidMiner framework to perform data analytics.
9m
| 9 questions
SKILL BENCHMARKS INCLUDED
Machine Learning Using SQL with BigQuery ML Awareness (Entry Level)
The Machine Learning Using SQL with BigQuery ML Awareness (Entry Level) benchmark measures your ability to identify how to use BigQuery to work with data and machine learning models. You will be evaluated on your recognition of different kinds of machine learning (ML) models and use cases for regression analysis. A learner who scores high on this benchmark demonstrates that they have a basic understanding of how to use the BigQuery framework to perform data analytics.
11m 58s
| 12 questions
SKILL BENCHMARKS INCLUDED
Low-code Machine Learning with KNIME Competency (Intermediate Level)
The Low-code Machine Learning with KNIME Competency (Intermediate Level) benchmark measures your knowledge of creating KNIME data workflows and managing regression models. You will be evaluated on your skills in building classification models, clustering models in KNIME, and performing time series analysis. A learner who scores high on this benchmark demonstrates that they have good competency in using KNIME to perform data analytics.
24m
| 24 questions
SKILL BENCHMARKS INCLUDED
No-code Machine Learning with RapidMiner Competency (Intermediate Level)
The No-code Machine Learning with RapidMiner Competency (Intermediate Level) benchmark measures your ability to use Turbo Prep for data preparation and Auto Model for model building. You will be evaluated on your ability to train regression and classification models, perform hyperparameter tuning, deploy models to a local machine, use and evaluate clustering models, and perform windowing, differencing, and moving average computations on time series data. A learner who scores high on this benchmark demonstrates that they have good competency in using RapidMiner to perform data analytics.
27m
| 27 questions
SKILL BENCHMARKS INCLUDED
Machine Learning Using SQL with BigQuery ML Competency (Intermediate Level)
The Machine Learning Using SQL with BigQuery ML Competency (Intermediate Level) benchmark measures your ability to work with data using the bq command-line tool, Looker Studio, and DataPrep and train binary and multi-class classification models. You will be evaluated on your recognition of use cases for training clustering models and recommendation systems and time series analysis forecasting, knowledge of the components in an autoregressive integrated moving average (ARIMA) model, and ability to perform time series analysis on data. A learner who scores high on this benchmark demonstrates that they have good experience using the BigQuery framework to perform data analytics.
17m
| 17 questions
