Python Machine Learning By Example – Second Edition

Python Machine Learning By Example, 2nd Edition

eBook Details:

  • Paperback: 382 pages
  • Publisher: WOW! eBook; 2nd edition (February 28, 2019)
  • Language: English
  • ISBN-10: 1789616727
  • ISBN-13: 978-1789616729

eBook Description:

Python Machine Learning By Example, 2nd Edition: Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.

Python Machine Learning By Example, Second Edition begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.

With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.

By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

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1 Response

  1. May 13, 2019

    […] Python Machine Learning Cookbook, 2nd Edition: Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch […]

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