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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)

4.7 3,303 ratings

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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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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 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 Tensor Flow 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.

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).

ISBN-10

1492032646
ISBN-13

978-1492032649
Edition

2nd
Publisher

O'Reilly Media
Publication date

October 15, 2019
Language

English
Dimensions

7 x 1.5 x 9.5 inches
Print length

856 pages
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Product details

Publisher ‏ : ‎ O'Reilly Media; 2nd edition (October 15, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 856 pages
ISBN-10 ‏ : ‎ 1492032646
ISBN-13 ‏ : ‎ 978-1492032649
Item Weight ‏ : ‎ 2.72 pounds
Dimensions ‏ : ‎ 7 x 1.5 x 9.5 inches

Best Sellers Rank: #76,013 in Books (See Top 100 in Books)
- #26 in Computer Neural Networks
- #82 in Python Programming
- #139 in Artificial Intelligence & Semantics

Customer Reviews:
4.7 3,303 ratings

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Aurélien Géron

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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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Amazon Customer

Terrific ML book, and one of my favorite programming books in general

Reviewed in the United States on April 8, 2023

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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!

6 people found this helpful

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Rachel Cyr

The Best Textbook I've Ever Bought

Reviewed in the United States on June 14, 2021

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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!

5 people found this helpful

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Amazon Customer

Excellent book

Reviewed in the United States on September 19, 2020

Verified Purchase

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.

2 people found this helpful

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Jim Lewis

Publication Quality on My Print Copy is GREAT!

Reviewed in the United States on November 25, 2019

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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!

15 people found this helpful

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Ashis

Nice ML book, but not for a beginner

Reviewed in the United States on July 19, 2022

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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.

4 people found this helpful

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Marcelo Bezerra de Noronha

Livro bastante objetivo

Reviewed in Brazil on April 10, 2023

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O livro apresenta aspectos teóricos em conjunto com a prática de programação que tem suas funções explicadas claramente .. ótimo livro de suporte

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Lalirri

Impresión a color

Reviewed in Mexico on February 23, 2023

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Llego en perfectas condiciones y la impresión como debe ser, a color!!

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S.L.

Content is great but the book looks used

Reviewed in Canada on November 16, 2022

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I am not happy about the condition of the book. It was not sealed and looked used. I could have returned it but I didn't because of environmental reasons. I believe that is not fair to downrate the content of the book because of its look. My rating of the book is 5 stars and 3 stars for the seller.

One person found this helpful

H.P.J.M.

Fabulous book - jam-packed

Reviewed in the United Kingdom on September 18, 2023

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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.

Pablo Jordán de la Fuente

Me gustó bastante

Reviewed in Spain on September 8, 2023

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