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Practical Machine Learning Tools and Techniques, Second Edition: A Comprehensive Guide for Python Developers

Yasunari Kawabata

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Published in Data Mining: Practical Machine Learning Tools And Techniques Second Edition (The Morgan Kaufmann In Data Management Systems)

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Machine learning is a rapidly growing field that has the potential to transform many aspects of our lives. From self-driving cars to medical diagnosis, machine learning algorithms are already being used to solve a wide range of problems. To harness the power of machine learning, however, it is essential to have a strong understanding of the underlying tools and techniques.

Practical Machine Learning Tools and Techniques, Second Edition provides a comprehensive to machine learning for Python developers. The book covers a wide range of topics, including:

Data Mining: Practical Machine Learning Tools and Techniques Second Edition (The Morgan Kaufmann in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)

by Ian H. Witten

4 out of 5

Language	:	English
File size	:	8657 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	560 pages

* Data preprocessing and feature engineering * Model selection and evaluation * Supervised and unsupervised learning algorithms * Advanced topics such as natural language processing, computer vision, and time series analysis

The second edition of Practical Machine Learning Tools and Techniques has been updated to include the latest advances in the field of machine learning. The book also includes new chapters on deep learning and reinforcement learning.

This book is a valuable resource for anyone who wants to learn more about machine learning and how to apply it to real-world problems. Whether you are a beginner or an experienced practitioner, Practical Machine Learning Tools and Techniques, Second Edition will provide you with the knowledge and skills you need to build successful machine learning models.

What's New in the Second Edition?

The second edition of Practical Machine Learning Tools and Techniques has been updated to include the following new material:

* A new chapter on deep learning * A new chapter on reinforcement learning * Coverage of new algorithms and techniques, such as gradient boosting and random forests * Updated code examples and exercises * A new appendix that provides a brief overview of mathematics for machine learning

Who Should Read This Book?

Practical Machine Learning Tools and Techniques, Second Edition is written for Python developers who want to learn about machine learning. The book is also a valuable resource for anyone who wants to update their skills in machine learning or who needs to learn about the latest advances in the field.

About the Authors

Mark Hall is a machine learning researcher and author. He has over 20 years of experience in the field of machine learning, and he has published numerous papers and books on the topic.

Eibe Frank is a professor of computer science at the University of Waikato. He has over 20 years of experience in the field of machine learning, and he has published numerous papers and books on the topic.

Geoffrey Holmes is a professor of computer science at the University of Waikato. He has over 20 years of experience in the field of machine learning, and he has published numerous papers and books on the topic.

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)

by Ian H. Witten

4 out of 5