Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition - Softcover

Raschka; Sebastian; Mirjalili; Vahid

Softcover

ISBN 10: 1789955750 ISBN 13: 9781789955750

Publisher: Packt Publishing, 2019

Synopsis

Applied machine learning with a solid foundation in theory. Revised and expanded with TensorFlow 2, GANs, and reinforcement learning.

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 machine learning in Python
• Fully updated and expanded to cover Generative Adversarial Network (GAN) models, reinforcement learning, TensorFlow 2, and modern best practice

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a clear 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 worked examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

This new third edition is updated for TensorFlow 2.0 and the latest additions to scikit-learn. It's expanded to cover cutting-edge reinforcement learning techniques based on deep learning as well as an introduction to Generative Adversarial Networks.

This book is your companion, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

• Understand the key frameworks in data science, machine learning, and deep learning
• Harness the power of the latest Python open source libraries in machine learning
• Explore machine learning techniques using challenging real-world data
• Master deep neural network implementation using the TensorFlow library
• Learn the mechanics of classification algorithms to implement the best tool for the job
• Predict continuous target outcomes using regression analysis
• Uncover hidden patterns and structures in data with clustering
• Delve 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 and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.

About this title

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.

Among Sebastian's other works is his book "Python Machine Learning," which introduced people to the practical and theoretical aspects of machine learning around the globe with translations into German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.

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.

Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.

While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.

Edition details

• Publisher: Packt Publishing
• Publication date: 2019
• ISBN 10: 1789955750
• ISBN 13: 9781789955750
• Binding: Paperback
• Language: English
• Edition number: 3
• Number of pages: 770