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O'Reilly

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 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 Read more

Publisher ‏ : ‎ O'Reilly Media


Publication date ‏ : ‎ October 15, 2019


Edition ‏ : ‎ 2nd


Language ‏ : ‎ English


Print length ‏ : ‎ 848 pages


ISBN-10 ‏ : ‎ 1492032646


ISBN-13 ‏ : ‎ 49


Item Weight ‏ : ‎ 2.85 pounds


Dimensions ‏ : ‎ 7 x 1.5 x 9.5 inches


Best Sellers Rank: #65,513 in Books (See Top 100 in Books) #12 in Computer Vision & Pattern Recognition #17 in Data Processing #28 in Computer Neural Networks


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Top Amazon Reviews

    • Usefull book
Format: Paperback
A good and interesting book about a special and very important topic.
Reviewed in the United States on July 16, 2026 by Oscar
    • Best book to learn Machine Learning from the scratch
Format: Paperback
Review Image by Adarsh
Review Image by Adarsh
Review Image by Adarsh
Received, haven’t completed yet but the content is pretty good. It is a combination of theory and practicals. Topics explained with examples and provided codes for practice on terminal as you read. The chapters have questions so you can treat it as a course. In the end, there is project checklist to create a project and other appendix. I am not sure if the content will be too hard or too theoretical later but for now it looks good. ... show more
Reviewed in the United States on May 2, 2026 by Adarsh
    • The Best Textbook I've Ever Bought
Format: Paperback
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
    • Excellent book
Format: Paperback
I am only about to start with chapter 4 but if the rest of the book is of the same quality as the first few chapters then it definitely deserves 5 stars. The title of the book covers the content, and the book comes loaded with practical advice as well as working code samples. In fact is comes with complete projects in the form of Jupyter Notebooks. You really cannot go wrong buying this book, certainly given the price. Even 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. That is partly because of the nature of the material, and partly because the author thankfully goes into the technical details of the what and how (and does so in a very accessible way). There is no "handwaving"! As a result the text is a bit dense and it can make for slow reading, but on the other hand it then leaves you with the satisfaction of a rather good understanding of the topic. One more thing - it probably does not hurt to be well versed in Pandas, especially matrix-wide operations in a single line of code. ... show more
Reviewed in the United States on September 19, 2020 by Amazon Customer
    • A Good Resource for Classical ML. Complement It with "Deep Learning from Curiosity to Mastery" Book
Format: Paperback
Hands-On Machine Learning is a comprehensive resource for learning classical machine learning with Scikit-Learn. It covers essential topics such as regression, classification, support vector machines, decision trees, ensemble methods, dimensionality reduction, and clustering. It also introduces deep learning using Keras and TensorFlow, but its broader emphasis is machine learning. Readers who want to specialize in neural networks and use PyTorch instead of TensorFlow may benefit from complementing it with the "Deep Learning from Curiosity to Mastery" series. The series focuses specifically on deep learning, beginning with neural-network foundations in Volume 1 and progressing in Volume 2 to CNNs, RNNs, LSTMs, embeddings, attention, Transformers, and emerging AI frontiers using PyTorch. The two resources complement each other well: Géron provides the broader machine-learning foundation, while Deep Learning from Curiosity to Mastery offers a focused, intuition-first path into deep learning using PyTorch. ... show more
Reviewed in the United States on August 11, 2026 by Adam
    • Terrific ML book, and one of my favorite programming books in general
Format: Paperback
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! ... show more
Reviewed in the United States on April 8, 2023 by Amazon Customer
    • Publication Quality on My Print Copy is GREAT!
Format: Paperback
The book was worth the wait! The publication quality of the print edition is great. Love the color illustrations. The one thing that I miss is that having bought the print edition, it would be sweet to have an offer to acquire the electronic edition at a reduced price but since Amazon now seems to be handling O'Reilly book sales and probably wants to sell as many Kindle editions as possible, a PDF copy of Hands-On Machine Learning, 2nd Ed., does not seem to be in my future at a bargain price. My review is preliminary - I've read bits of the online draft version-and the clarity and superb organization of Géron's writing convinced me that I wanted a finished copy of the book. My current avocational interest is Reinforcement Learning and Géron gives an excellent overview - to dive deep, one would probably still want to refer to Sutton & Barto's 2nd Ed. book (available on Amazon or for free online) or David Silver's excellent 2015 UCL lectures, also available online.. I will slowly work my way through Géron's book in its entirety but my primary reason for owning the book is as a reference. It makes a great roadmap to the current state of machine learning and, best of all, it makes learning about ML fun! ... show more
Reviewed in the United States on November 25, 2019 by Jim Lewis
    • Must have to get a FLAG machine learning position; Much better than 1st edition
Format: Paperback
I took a machine learning graduate course in my master program. I had a top conference paper. The professor used 1st edition of this book as one textbook for the course. I had a 1st edition of the book but did not have time to read. Now I buy the 2nd edition because the Tensorflow 2 has merged with Keras, which means we can avoid to learn the hard syntax of tensorflow 1.0, and there are a lot of new advances in machine learning, such as generative models. Also to my surprise, the book is colorful. That makes the book is more interesting. Each chapter has summary of math. That is better than some programming machine learning books that do not have any math. If you have some backgrounds in math of machine learning, this book can save you time because it gives you the whole picture without lost. If you are very interested in some equations and want to derive them, you can use Pattern Recognition and Machine Learning book. The Github has a lot of python projects of machine learning. The codes are well-written. If you can write codes like the codes in the projects, you will have the potential to enter Google. Go Google, the book is a must have. ... show more
Reviewed in the United States on February 1, 2020 by Christ

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