Free eBook: Python Machine Learning - Third Edition

Python Machine Learning - Third Edition
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Sebastian Raschka and Vahid Mirjalili, 770 pages, Dec 2019

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

Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. This new third edition is updated for TensorFlow 2 and the latest additions to scikit-learn. It’s expanded to cover two cutting edge machine learning techniques: reinforcement learning and Generative Adversarial Networks.

Register now to access this free eBook

Your password must have at least 8 characters, one uppercase, one lowercase and one number.

By signing up, you are confirming you would like to receive occasional emails about special offers and recommendations.

Chapters

Chapter 1

Free

Giving Computers the Ability to Learn from Data

Goes back to the origin of machine learning and introduces binary perceptron classifiers and adaptive linear neurons. This chapter is a gentle introduction to the fundamentals of pattern classification and focuses on the interplay of optimization algorithms and machine learning.

Chapter 2

Free

Training Simple Machine Learning Algorithms for Classification

Describes the essential machine learning algorithms for classification and provides practical examples using one of the most popular and comprehensive open source machine learning libraries, scikit-learn.

Chapter 3

Free

A Tour of Machine Learning Classifiers Using scikit-learn

Describes the essential machine learning algorithms for classification and provides practical examples using one of the most popular and comprehensive open source machine learning libraries, scikit-learn.

Chapter 4

Free

Building Good Training Datasets – Data Preprocessing

Discusses how to deal with the most common problems in unprocessed datasets, such as missing data. It also discusses several approaches to identify the most informative features in datasets and how to prepare variables of different types as proper inputs for machine learning algorithms.

Chapter 5

Free

Compressing Data via Dimensionality Reduction

Describes the essential techniques to reduce the number of features in a dataset to smaller sets, while retaining most of their useful and discriminatory information. It also discusses the standard approach to dimensionality reduction via principal component analysis and compares it to supervised...

Chapter 6

Free

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Discusses the dos and don'ts for estimating the performance of predictive models. Moreover, it discusses different metrics for measuring the performance of our models and techniques for fine-tuning machine learning algorithms.

Related Titles

Hands-On Reinforcement Learning with Python

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Hands-On Reinforcement Learning with R

Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and Op...

Deep Learning with TensorFlow 2 and Keras - Second Edition

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices

Discover the new Packt free eBook range