AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty
- 18 Courses | 14h 25m 5s
- 1 Book | 7h
Discover how to build, train, tune, and deploy machine learning models using the AWS Cloud as you prepare for the AWS Certified Machine Learning - Specialty certification exam.
GETTING STARTED
AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
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1m 8s
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5m 40s
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Badge
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
Assessment
Badge
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
Badge
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
Badge
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Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) ExamAWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.
7h
By Shreyas Subramanian, Stefan Natu
SKILL BENCHMARKS INCLUDED
AWS Certified Machine Learning Specialty: Exploratory Data Analysis Competency (Intermediate Level)
The AWS Certified Machine Learning Specialty: Exploratory Data Analysis Competency benchmark measures your ability to explore, sanitize, and prepare data for modeling, perform feature engineering, and analyze and visualize data for machine learning. A learner who scores high on this benchmark demonstrates that they have the necessary skills to identify and handle dirty data, transform data, recognize labeled data and identify migration strategies, identify and extract features from data sets, graph data, interpret descriptive statistics, and perform clustering.
34m
| 34 questions
AWS Certified Machine Learning Specialty: Modeling Competency (Intermediate Level)
The AWS Certified Machine Learning Specialty: Modeling Competency benchmark measures your ability to frame business problems as machine learning problems, select and train appropriate machine learning models for the problem, perform hyperparameter optimizations for tuning the model, and evaluate the models. A learner who scores high on this benchmark demonstrates that they have the skills necessary to build and develop machine learning solutions on AWS.
34m
| 34 questions
AWS Certified Machine Learning Specialty: Implementation and Operations Competency (Intermediate Level)
The AWS Certified Machine Learning Specialty: Implementation and Operations Competency benchmark measures your ability to build machine learning solutions for performance, availability, scalability, and fault tolerance. A learner who scores high on this benchmark demonstrates that they have the skills to recommend appropriate machine learning algorithms for a given problem and apply basic AWS security practices to machine learning solutions.
31m
| 31 questions
AWS Certified Machine Learning Specialty: Data Engineering Competency (Intermediate Level)
The AWS Certified Machine Learning Specialty: Data Engineering Competency benchmark measures your ability to create data repositories for machine learning, identify and implement data ingestion solutions, and identify and implement a data transformation solution. This competency tests your skills in identifying data sources, determining storage mediums, implementing data ingestion pipelines using AWS Kinesis, and your ability to identify data transformation solutions with Amazon Glue/EMR/Batch. A learner who scores high on this benchmark demonstrates that they have the necessary data engineering skills to implement machine learning solutions on AWS.
27m
| 27 questions
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