Python Machine Learning - Third Edition

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  • 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

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.

  • 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
Page Count 554
Course Length 16 hours 37 minutes
ISBN 9781789955750
Date Of Publication 20 Oct 2019