Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners
- 5h 20m
- Ekaba Bisong
- Apress
- 2019
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform.
Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP.
What You’ll Learn
- Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
- Know the programming concepts relevant to machine and deep learning design and development using the Python stack
- Build and interpret machine and deep learning models
- Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products
- Be aware of the different facets and design choices to consider when modeling a learning problem
- Productionalize machine learning models into software products
Who This Book Is For
Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
About the Author
Ekaba Bisong is a Data Science Lead at T4G. He previously worked as a data scientist/data engineer at Pythian. In addition, he maintains a relationship with the Intelligent Systems Labs at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer and a Google Developer Expert in machine learning.
In this Book
-
What is Cloud Computing?
-
An Overview of Google Cloud Platform Services
-
The Google Cloud SDK and Web CLI
-
Google Cloud Storage (GCS)
-
Google Compute Engine (GCE)
-
JupyterLab Notebooks
-
Google Colaboratory
-
What is Data Science?
-
Python
-
NumPy
-
Pandas
-
Matplotlib and Seaborn
-
What is Machine Learning?
-
Principles of Learning
-
Batch vs. Online Learning
-
Optimization for Machine Learning: Gradient Descent
-
Learning Algorithms
-
Introduction to Scikit-learn
-
Linear Regression
-
Logistic Regression
-
Regularization for Linear Models
-
Support Vector Machines
-
Ensemble Methods
-
More Supervised Machine Learning Techniques with Scikit-learn
-
Clustering
-
Principal Component Analysis (PCA)
-
What is Deep Learning?
-
Neural Network Foundations
-
Training a Neural Network
-
TensorFlow 2.0 and Keras
-
The Multilayer Perceptron (MLP)
-
Other Considerations for Training the Network
-
More on Optimization Techniques
-
Regularization for Deep Learning
-
Convolutional Neural Networks (CNN)
-
Recurrent Neural Networks (RNNs)
-
Autoencoders
-
Google BigQuery
-
Google Cloud Dataprep
-
Google Cloud Dataflow
-
Google Cloud Machine Learning Engine (Cloud MLE)
-
Google AutoML: Cloud Vision
-
Google AutoML: Cloud Natural Language Processing
-
Model to Predict the Critical Temperature of Superconductors
-
Containers and Google Kubernetes Engine
-
Kubeflow and Kubeflow Pipelines
-
Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines
