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

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

by Aurélien Géron (Author)
4.8 out of 5 stars (3,445)
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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 frameworksâ??Scikit-Learn and TensorFlowâ??author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youâ??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 youâ??ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use Scikit-Learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets

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  1. ISBN-10
    1492032646
  2. ISBN-13
    978-1492032649
  3. Edition
    2nd
  4. Publisher
    O'Reilly Media
  5. Publication date
    October 15, 2019
  6. Language
    English
  7. Dimensions
    7 x 1.5 x 9.5 inches
  8. Print length
    848 pages
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Editorial Reviews

About the Author

Aurélien Géron is a machine learning consultant and trainer. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst (a leading Wireless ISP in France) from 2002 to 2012, and a founder and CTO of two consulting firms -- Polyconseil (telecom, media and strategy) and Kiwisoft (machine learning and data privacy).

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ October 15, 2019
  • Edition ‏ : ‎ 2nd
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 848 pages
  • ISBN-10 ‏ : ‎ 1492032646
  • ISBN-13 ‏ : ‎ 978-1492032649
  • Item Weight ‏ : ‎ 2.85 pounds
  • Dimensions ‏ : ‎ 7 x 1.5 x 9.5 inches
  • Best Sellers Rank: #162,935 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.8 out of 5 stars (3,445)

About the author

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Aurélien Géron

Aurélien Géron

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.

Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.

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Customer reviews

4.8 out of 5 stars
3,445 global ratings
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Customers say

Customers find this machine learning book excellent at explaining topics and providing powerful tools for implementing models. The content is comprehensive, with thorough coverage of complex subjects, and customers appreciate its clear, simple language and readability. Moreover, the book receives positive feedback for its writing quality and value for money, with one customer noting the helpful Python 3 code available on Github.
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137 customers mention content, 123 positive, 14 negative
Customers find the book's content excellent, with one customer noting it provides a great introduction to data science principles and techniques.
Great book, very summarised and direct to the subject, it shows everything you need to initiate or improve your skills about data science.Read more
Good book. Has a nice mix of theory and applications with step by step instructions. Now with figures in color.Read more
Great content but similar to others the printing is awful. I’m only 40 pages in and several pages are in the wrong order!...Read more
...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.Read more
54 customers mention practical, 54 positive, 0 negative
Customers find this machine learning book practical and useful for learning the basics, providing powerful tools for implementing models, with one customer highlighting its comprehensive coverage of deep reinforcement learning.
Wonderful book! Just what I expected. Very practical, hands-on like the title says....Read more
One of the best reference book for Tensorflow 2.0Read more
Best book on machine learning for the begineer.Read more
The most useful book I have ever read on any subject. The quality of the material is extraordinary.Read more
24 customers mention comprehensive, 22 positive, 2 negative
Customers find the book comprehensive, with thorough coverage of complex topics and detailed explanations. One customer notes it includes extensive references to papers.
...Really comprehensive and easy to follow.Read more
I followed the book step-by-step. It's comprehensive and most of the code works....Read more
I am happy with the book, it covers a lot of topics. Good colors, good paper, thin.Read more
...I'm glad I decided to get a physical copy. Great explanations and background info, and clear coding examples....Read more
21 customers mention clarity, 18 positive, 3 negative
Customers find the book clear and easy to read, appreciating its simple language and well-organized tables. One customer notes that the author does not gloss over important details.
I have read many books, this one is very good. Clear, details, hands-on approach. I recommend it 100%.Read more
It's a beautiful book written with simple language and cute graphics....Read more
Very clear book with valuable applicable examples....Read more
...discussion of machine learning, including data preparation, visualization, splitting into train and test sets, model fitting, and evaluation....Read more
19 customers mention readability, 14 positive, 5 negative
Customers find the book easy to understand, with one mentioning that the model-by-model approach makes it simple to follow.
...great and even if one does not know python programming it is easy to follow along....Read more
...is very good and provides step by step instruction that makes it easy to follow and understand the concept behind each test....Read more
...One heads up is that it's not an easy read....Read more
...The book is easy to read and to understand (a fairly complex topic). It is an invaluable resource!Read more
19 customers mention writing quality, 18 positive, 1 negative
Customers find the book well written, with one customer noting that the examples are clear and another mentioning that the codes are well-structured.
Well written, intermediate level ML book.Read more
Well written book well balanced between technical vs. being descriptive.Read more
A nice educational, very well written and up to date overview of machine learning techniques + tons of practical and well documented code in python...Read more
Wonderfully written and code that you can download to follow along on your computer....Read more
13 customers mention code, 10 positive, 3 negative
Customers appreciate the code in the book, with one customer highlighting the real-life examples with Python code and another noting that the source code is fully disclosed in Python 3.
Wonderfully written and code that you can download to follow along on your computer....Read more
A nice combination of ML practice guidelines, source code, and ideas behind them.Read more
...Even if you're well veresed in modelling you'll learn some good coding techniques put in layman's terms.Read more
...I'm writing this review in July of 2021. Half of the code no longer compiles, or throws run-time exceptions and warnings....Read more
10 customers mention value for money, 8 positive, 2 negative
Customers find the book worth the money.
...if there are some chapters you end up liking less then it's still worth the money. One heads up is that it's not an easy read....Read more
...The book is not cheap, but the paper quality is the worse! I have bought cheaper books for a way better paper quality...Read more
...Definietly worth the price!Read more
Its quality is ok, but I think it is too expensiveRead more
Awesome book - 5/5
5 out of 5 stars
Awesome book - 5/5
The print quality is a great upgrade from the 1st edition and the GitHub support by the author is the icing on the cake. If you want to learn practical AI and ML, this is the book to go with.
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Top reviews from the United States

  • Amazon Customer
    5.0 out of 5 stars Terrific ML book, and one of my favorite programming books in general
    Reviewed in the United States on April 8, 2023
    Format: PaperbackVerified Purchase
    I've been following this book since its first edition, about time I write a review! It really does strike the perfect balance between code and theory. Everything is clear and written in a friendly tone. It'll get you started in applying everything from basic linear regression through decision tree, all the way to deep learning. My favorite is chapter 2, which is a step-by-step guide on exploring a data project, it's like having a professional guide you. I'm an experienced software developer, and I owe this book a lot for introducing me to many concepts. I'm old-school, so sitting down with a book and copying code examples takes me back and is a familiar experience. For some people, copy pasting might be more intuitive but you really can learn from doing things by hand. The full code is on github, but I recommend using it for reference only. What this book isn't, and doesn't pretend to be, is an introduction to Python. Some basic programming knowledge is needed, but if you want to work in the field, you'd need that anyway, and you shouldn't be afraid to dive into it. Looks like I'll be checking the 3rd edition!
    9 people found this helpful
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  • Shankar Saikia
    5.0 out of 5 stars Gold Medal Winner
    Reviewed in the United States on May 15, 2020
    Format: PaperbackVerified Purchase
    The Tokyo Olympics of 2020 got postponed to 2021. If there were a contest for best AI/ML book at the Olympics this year this book would have earned the gold medal ! I loved it so much that I read it at least twice, and each time I underlined/highlighted/took-notes. I love how lucidly the author explains concepts. He does an excellent job of explaining topics such as the model, the learning algorithm (also called the optimization algorithm), regularization hyperparameter, generalization etc. The examples are great and even if one does not know python programming it is easy to follow along. (I learned python a few months later, which made it even easier and more interesting to follow the examples in this and other books). While no one single book can teach one ML/AI, this book would make the Mount Rushmore of AI/ML books (along with (1) Intro to Statistical Learning by Hastie etc (2) Intro to Machine Learning by Alpaydin (3) Deep Learning by Goodfellow, Bengio etc). I highly recommend this book to anyone aspiring to get into the field of ML/AI.
    7 people found this helpful
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  • Jarrod and Sarah
    5.0 out of 5 stars Best Machine Learning book I own
    Reviewed in the United States on November 18, 2019
    Format: PaperbackVerified Purchase
    I'm very pleased with this book. I enjoy the little bits of humor here and there, and it does a great job not glossing over important details that might be a stumbling block for someone. I'm quite comfortable with python however I appreciated that he did go into depth on setting up virtual environments and best practices. I remember years back when I was starting that whole concept tripped me up so much, having this explained so well is going to save someone a lot of time. Also his code seems so far to be written in a very thoughtful way and has them all on github. He also goes into lots of gotchas and tips and tricks that just overall seem to add a certain maturity to his writing. He has obviously very well versed in machine learning.

    Overall I would recommend. It's been much more interesting than I expected.
    35 people found this helpful
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  • Siddharth Saran
    5.0 out of 5 stars An excellent introduction to scikit-learn, keras and tensorflow
    Reviewed in the United States on March 19, 2021
    Format: PaperbackVerified Purchase
    This is an excellent book for an introduction to Keras and Tensorflow. It complements the Coursera Tensorflow course and the tutorials on the Tensorflow website very well.

    At first, I didn’t appreciate that the first half of the book is devoted to machine learning. But after reading that part, I learnt many new tricks/shortcuts. For example, how easy it is to do stratified shuffle spits to balance out the training and test samples and creating pipelines. The book also reenforces a process for ML, which I really liked.

    The deep learning part of the book is excellent as well. It has the right balance between theory and practical ways to use Tensorflow.

    Having the code available on Github is very helpful.

    The book is easy to read and to understand (a fairly complex topic). It is an invaluable resource!
    2 people found this helpful
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  • Ashis
    4.0 out of 5 stars Nice ML book, but not for a beginner
    Reviewed in the United States on July 19, 2022
    Format: PaperbackVerified Purchase
    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.
    5 people found this helpful
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  • Rachel Cyr
    5.0 out of 5 stars The Best Textbook I've Ever Bought
    Reviewed in the United States on June 14, 2021
    Format: PaperbackVerified Purchase
    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!
    9 people found this helpful
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  • Zebra
    5.0 out of 5 stars New content/topics explained well
    Reviewed in the United States on March 20, 2025
    Format: PaperbackVerified Purchase
    Fantastic content with valuable examples.
    One person found this helpful
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  • Pedro
    5.0 out of 5 stars Livro excepcional
    Reviewed in Brazil on July 14, 2025
    Format: PaperbackVerified Purchase
    Livro excelente e muito bem didático.
    Report
  • H.P.J.M.
    5.0 out of 5 stars Fabulous book - jam-packed
    Reviewed in the United Kingdom on September 18, 2023
    Format: PaperbackVerified Purchase
    This book should be regarded as a "gold-standard" for technical books. It balances theory and practice, has exercises (actually with answers!) and covers a tremendous breadth and depth.

    The book starts out in a refreshingly unconventional way of giving you a crash course in ML concepts before diving in to an end-to-end project. I note that one reviewer didn't like that but I liked it a lot. While a lot of it will go over your head if you lack experience (and the author assumes you don't have much), it gives you appreciation of what an overall real-life project might look like. The rest of the book is spent unpacking each of those stages.

    The first part of the book looks at more "classical" or traditional machine learning concepts like linear regression, logistic regression, SVMs, decision trees, ensemble learning and unsupervised models. Along the way you learn a lot of data science best-practises and how to train and test things properly.

    The second part dives into deep learning, progressing from general neural networks to CNNs, RNNs, LSTMs, autoencoders and GANs. You get a flavour of how GPT models work. Other topics covered in this section are Tensorflow and Keras (including a part on deploying models) and a chapter on another paradigm: reinforcement learning.

    Geron doesn't shy away from the math but gives you enough theory to appreciate the detail if you like that, and explains it in intuitive ways and with code. Some of the formulas can look intimidating but they are unpacked and explained well.

    There are review questions and/or exercises at the end of each chapter. One of my biggest frustrations with technical books in general is when they give you questions but no answers. Here, you get answers and also worked code in the provided notebooks, which is amazing. Other technical authors: take note. The exercises are often quite challenging to implement or at least open-ended, but I believe that to be a good thing. I learnt a lot from doing them (I'll admit I didn't do all of them!).

    The writing is clear, engaging and often humourous.

    To sum up, if you want to learn more about ML, I highly recommend this book. This review is for the 2nd edition but I'll be buying the 3rd edition and will definitely be re-reading. There is so much great information to take in. Thanks to the author for this masterpiece.
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  • Braden
    5.0 out of 5 stars Great resource
    Reviewed in Canada on July 24, 2025
    Format: PaperbackVerified Purchase
    Excellent book for getting into machine learning. Plenty of example code.
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  • Dr. WilsonLiao
    5.0 out of 5 stars Great Job. Good Book received in wonderful good conditions due to good packaging done.
    Reviewed in Singapore on December 23, 2022
    Format: PaperbackVerified Purchase
    Good Packaging done. Great Job.
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  • Ibadurrahman
    5.0 out of 5 stars Worth your money
    Reviewed in Japan on December 15, 2020
    Format: PaperbackVerified Purchase
    This second edition book is totally worth your money
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