Book description
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.
Purchase of the print or Kindle book includes a free eBook in the PDF format.
Key Features
 Third edition of the bestselling, widely acclaimed Python machine learning book
 Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
 Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices
Book Description
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a stepbystep tutorial, and a reference you'll keep coming back to as you build your machine learning systems.
Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.
Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikitlearn. It's also expanded to cover cuttingedge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.
This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
What you will learn
 Master the frameworks, models, and techniques that enable machines to 'learn' from data
 Use scikitlearn for machine learning and TensorFlow for deep learning
 Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
 Build and train neural networks, GANs, and other models
 Discover best practices for evaluating and tuning models
 Predict continuous target outcomes using regression analysis
 Dig deeper into textual and social media data using sentiment analysis
Who this book is for
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
Table of contents
 Preface

Giving Computers the Ability to Learn from Data
 Building intelligent machines to transform data into knowledge
 The three different types of machine learning
 Introduction to the basic terminology and notations
 A roadmap for building machine learning systems
 Using Python for machine learning
 Summary
 Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using scikitlearn
 Choosing a classification algorithm
 First steps with scikitlearn – training a perceptron
 Modeling class probabilities via logistic regression
 Maximum margin classification with support vector machines
 Solving nonlinear problems using a kernel SVM
 Decision tree learning
 Knearest neighbors – a lazy learning algorithm
 Summary
 Building Good Training Datasets – Data Preprocessing
 Compressing Data via Dimensionality Reduction
 Learning Best Practices for Model Evaluation and Hyperparameter Tuning
 Combining Different Models for Ensemble Learning
 Applying Machine Learning to Sentiment Analysis
 Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis
 Introducing linear regression
 Exploring the Housing dataset
 Implementing an ordinary least squares linear regression model
 Fitting a robust regression model using RANSAC
 Evaluating the performance of linear regression models
 Using regularized methods for regression
 Turning a linear regression model into a curve – polynomial regression
 Dealing with nonlinear relationships using random forests
 Summary
 Working with Unlabeled Data – Clustering Analysis
 Implementing a Multilayer Artificial Neural Network from Scratch
 Parallelizing Neural Network Training with TensorFlow

Going Deeper – The Mechanics of TensorFlow
 The key features of TensorFlow
 TensorFlow's computation graphs: migrating to TensorFlow v2
 TensorFlow Variable objects for storing and updating model parameters
 Computing gradients via automatic differentiation and GradientTape
 Simplifying implementations of common architectures via the Keras API
 TensorFlow Estimators
 Summary
 Classifying Images with Deep Convolutional Neural Networks
 Modeling Sequential Data Using Recurrent Neural Networks
 Generative Adversarial Networks for Synthesizing New Data

Reinforcement Learning for Decision Making in Complex Environments
 Introduction – learning from experience
 The theoretical foundations of RL
 Reinforcement learning algorithms
 Implementing our first RL algorithm
 Chapter and book summary
 Other Books You May Enjoy
 Index
Product information
 Title: Python Machine Learning  Third Edition
 Author(s):
 Release date: December 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789955750
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