Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step 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 scikit-learn. It's also expanded to cover cutting-edge 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.

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

What you will learn

• Master the frameworks, models, and techniques that enable machines to 'learn' from data
• Use scikit-learn 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

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using scikit-learn
4. Building Good Training Datasets – Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data – Clustering Analysis
12. Implementing a Multilayer Artificial Neural Network from Scratch
13. Parallelizing Neural Network Training with TensorFlow

(N.B. Please use the Look Inside option to see further chapters)

What are the key takeaways from your book?

Machine learning can be useful in almost every problem domain. We cover a lot of different subfields of machine learning in the book. My hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications. Also, using well-developed and maintained open source software makes machine learning very accessible to a wide audience of experienced programmers, as well as those who are new to programming.

Python Machine Learning Third Edition is also different from a classic academic machine learning textbook due to its emphasis on practical code examples. However, I think this approach is highly valuable for both students and young researchers who are getting started in machine learning and deep learning. We heard from readers of previous editions that the book strikes a good balance between explaining the broader concepts supported with great hands-on examples, giving a light introduction to the mathematical underpinnings.

Why is it important to learn about GANs and reinforcement learning?

The first GANs paper had just come out two years before we started working on the second edition, but we weren't sure of its relevance. However, GANs have evolved into one of the hottest and most widely used deep learning techniques. People use them for creating artwork, colorizing and improving the quality of photos, and to recreate old video game textures in higher resolutions. It goes without saying that an introduction to GANs was long overdue.

Another important machine learning topic not included in previous editions is reinforcement learning, which has received a massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo and AlphaGo Zero, reinforcement learning has received extensive news coverage. And just recently, it’s been used to compete with the world's top e-sports players in the real-time strategy video game StarCraft II. We hope that our new chapters can provide an accessible and practical introduction to this exciting field.

Review

"Python Machine Learning 3rd edition is a very useful book for machine learning beginners all the way to fairly advanced readers, thoroughly covering the theory and practice of ML, with example datasets, Python code, and good pointers to the vast ML literature about advanced issues."

Alex Martelli, Python Software Foundation Fellow, Co-author of Python Cookbook and Python in a Nutshell

"A brilliantly approachable introduction to machine learning with Python. Raschka and Mirjalili break difficult concepts down into language the layperson can easily understand while placing these examples within real-world contexts. A worthy addition to your machine learning library!"

Dr Kirk Borne, Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton, and co-author of Ten Signs of Data Science Maturity

"Python Machine Learning, Third Edition is a highly practical, hands-on book that covers the field of machine learning, from theory to practice. I strongly recommend it to any practitioner who wishes to become an expert in machine learning. Excellent book!"

Sebastian Thrun, CEO of Kitty Hawk Corporation, and chairman and co-founder of Udacity

"I've been teaching "Big Data Machine Learning AI" at Johns Hopkins Carey Business School for the past several years and have employed Sebastian Raschka and Vahid Mirjalili's book ever since. I give their newest edition the highest marks for making Machine Learning digestible for the lay person. Their book is a must-have when teaching new recruits the amazing art of AI - I give their book my most enthusiastic endorsement!"

Jim Kyung-Soo Liew, Ph.D., Associate Professor in Finance and AI at Johns Hopkins Carey Business School

About the Author

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.

Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.