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O'Reilly Media

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end- to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the Tensor Flow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets. Read more


Publisher ‏ : ‎ O'Reilly Media; 2nd edition (October 15, 2019)


Language ‏ : ‎ English


Paperback ‏ : ‎ 856 pages


ISBN-10 ‏ : ‎ 1492032646


ISBN-13 ‏ : ‎ 49


Item Weight ‏ : ‎ 2.8 pounds


Dimensions ‏ : ‎ 7 x 1.2 x 9.2 inches


Best Sellers Rank: #8,287 in Books (See Top 100 in Books) #3 in Computer Neural Networks #10 in Artificial Intelligence & Semantics #11 in Python Programming


#3 in Computer Neural Networks:


#10 in Artificial Intelligence & Semantics:


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Top Amazon Reviews

    • Very clear and practical
Very clear book with valuable applicable examples. Even if you're well veresed in modelling you'll learn some good coding techniques put in layman's terms.
Reviewed in the United States on January 14, 2023 by phil ohana
    • Best book on machine learning for the begineer.
You need to know Python first, however, once you get beyond that, the book is very useful to start.
Reviewed in the United States on December 5, 2022 by Stephen E. Morris
    • Best book for Machine Learning. Hands Down.
The book provides a comprehensive insight and an in-depth analysis of the core of Machine Learning. On the seller, I would say they are full responsible and trustworthy.
Reviewed in the United States on October 28, 2022 by Xavier Romero
    • Nice ML book, but not for a beginner
This book covers many topics of ML and explains them with good examples. However, I believe it should be a little bit tough for a beginner. Similarly, it could not be the best book for an advanced reader because it gives pointers for advanced topics but does not go in-depth like mathematical explanation. In summary, it is an excellent book if you are looking for real-life examples with python code and you have a good basic idea in ML. ... show more
Reviewed in the United States on July 19, 2022 by Ashis
    • The Best Textbook I've Ever Bought
I'm currently getting my MS in health data science and this was the book we had to get for my machine learning class. I was annoyed when the teacher said the class would be textbook heavy and he was only going lecture on high level concepts, I thought there was no way textbook would be able to a carry a class and boy was I wrong. This is hands down the best textbook I've ever bought! I never expected a data science text book to be easy to read but this book flows so well!, its easily digestible and it gives great examples with data that is easily available. You can write completely functional ML code from this book alone but one of the best features is that the book has GitHub site broken down chapter by chapter that helps fill the code out. If you are someone like me who hadn't had any experience with Matplotlib the github was super helpful because it covers in depth how to make really nice plots for the various models. I would recommend this book to anyone who is doing machine learning. The only thing I would change about this book is when it gets into decision trees, RF, various boosting types, XGB, as it moves through the models it only gives an example of the classification form of the model or the regression for of the model and I think it would be helpful if it gave examples for both for each model. But with that being said this was a pretty minimal thing I would change and I would still buy the book again even if they didn't change it! It's definitely worth the money! ... show more
Reviewed in the United States on June 14, 2021 by Rachel Cyr
    • Non-deep learning part is excellent, but deep learning part is written in rush
I finished the whole book. Generally, this is an excellent book. I recommended it to everyone. The book has two parts. The first part is non-deep learning part, which is the best part. The second part is the deep learning part. In this part, some were written well, but some were written in rush (many details were missing). I wish the author gave more details on the deep learning models. All the codes can be run in colab without any error. ... show more
Reviewed in the United States on August 1, 2022 by Zorro
    • hands down one of the best education books I've ever had
My company was awarded an NSF grant which required me to VERY quickly brush up on machine learning. and man, did this book do a good job. I'm comfortable with many ML concepts, and have applied them to real world applications to great effect. This is a comprehensive and detailed guide. As a software engineer with 8 or so years of experience, I have to say the code snippet quality is as clean as it gets as well. The author nailed every aspect. Sometimes I don't really get a section until I'm reading it for the third time.. but that's just how understanding goes for me. Wish I had an equivalent book for different areas of study. ... show more
Reviewed in the United States on May 24, 2022 by nbrady
    • Outstanding book
I'm an experienced Ph.D.-level computer scientist, but have just started coding my first few machine learning applications (for computational biology research). This is by far the best of the half-dozen or so books I bought to help make the learning process faster and easier. It's very well written, and has a lot of clear, useful, well-organized information, and very little in the way of chatty, space-wasting, non-informative blather. I recommend it strongly. ... show more
Reviewed in the United States on July 11, 2022 by Michael Rosen

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