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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition

by Aurélien Géron (Author)

4.6 1,366 ratings

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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 TensorFlow―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 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
Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

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Prerequisites

This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.

Also, if you care about what’s under the hood you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).

More about this book

Machine Learning in Your Projects

Naturally you are excited about Machine Learning and you would love to join the party!

Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look.

For example:

Segment customers and find the best marketing strategy for each group
Recommend products for each client based on what similar clients bought
Detect which transactions are likely to be fraudulent
Predict next year’s revenue
And more!

Objective and Approach

This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data.

We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions.

Rather than implementing our own toy versions of each algorithm, we will be using actual production-ready Python frameworks:

Scikit-Learn

Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning.

TensorFlow

TensorFlow is a more complex library for distributed numerical computation using data flow graphs. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially thousands of multi-GPU servers. TensorFlow was created at Google and supports many of their large-scale Machine Learning applications. It was open-sourced in November 2015.

WARNING: California’s Proposition 65

Editorial Reviews

About the Author

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.

Product details

Publisher ‏ : ‎ O'Reilly Media; 1st edition (May 9, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 572 pages
ISBN-10 ‏ : ‎ 1491962291
ISBN-13 ‏ : ‎ 978-1491962299
Item Weight ‏ : ‎ 2.12 pounds
Dimensions ‏ : ‎ 6.75 x 1.5 x 9 inches

Best Sellers Rank: #365,738 in Books (See Top 100 in Books)
- #152 in Natural Language Processing (Books)
- #176 in Computer Neural Networks
- #816 in Artificial Intelligence & Semantics

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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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Customers find the book provides a clear overview of basic concepts and how to apply machine learning. They appreciate the concise writing style and easy-to-follow explanations. The book is useful for both researchers and practitioners, providing good examples and exercises for practice. Many consider it a valuable resource worth the purchase price. However, some customers report missing figures and images. Opinions vary on the math accuracy, with some finding it complex while others prefer simple equations.

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Understanding Writing quality Practical value Exercise content Value for money Math accuracy Color scheme Figure missing

184 customers mention "Understanding"167 positive17 negative

Customers find the book provides an overview of basic concepts and how to apply them. They appreciate the clear explanations and code implementations. The style is concise yet informative, making it suitable for self-study and quick development. Readers mention that the book teaches machine learning through projects rather than jargon. It goes into depth to teach readers about the mathematics of the software.

"...I would recommend it both for those wishing to self-study and quickly develop working models, and for students in machine learning who want to learn..." Read more

"...Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.) to get you started...." Read more

"...*, from a critical perspective, how to accomplish things like - selecting features, culling data, creating provably-suitable ML models, model/data..." Read more

"...This book is: - clearly written - teaches you machine learning through projects rather than jargon -..." Read more

70 customers mention "Writing quality"61 positive9 negative

Customers appreciate the book's clear writing style and practical approach. They find it concise and easy to read, with an intuitive structure that explains core machine learning concepts in detail. The examples and explanations are carefully constructed.

"...Beginners to machine learning will find it clear to follow and will be able to build complete systems within a few chapters while those with an..." Read more

"...to get you started. The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and..." Read more

"...I believe he has accomplished this goal very admirably. His easy style of writing encourages you to try things for yourself, and removes the worry..." Read more

"...This book is: - clearly written - teaches you machine learning through projects rather than jargon -..." Read more

42 customers mention "Practical value"42 positive0 negative

Customers find the book useful for both researchers and practitioners. They appreciate its practical approach, clear explanations, and hands-on exercises. The combination of theory and direct applications makes it an excellent resource.

"...through the chapters and the exercises and have found this book extremely useful." Read more

"...code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some...." Read more

"...Again - a very impressive feat, especially in light of the heady material being covered. Major Cudos by Mr. Géron!" Read more

"This book has been the greatest resource I've found to learn machine learning...." Read more

9 customers mention "Exercise content"9 positive0 negative

Customers find the book's exercises helpful for practical learning. They say it provides good examples of hands-on activities and is engaging to read.

"...+ Engaging: The book is a joy to read, and the author is quick to respond to issues pointed out by readers in the book or in the Jupyter Notebooks...." Read more

"...It gives good example exercises for practice." Read more

"...Also for teachers as a reference and to get good examples of hands-on activities, and perhaps, for more experienced users that want to enhance their..." Read more

"...Comes with exercises, both theorical and pratical." Read more

8 customers mention "Value for money"8 positive0 negative

Customers find the book offers good value for money. They say it explains the theory in simple terms and is worth the purchase price.

"...are worth the entire purchase price...." Read more

"...book cover to cover, the sections that I have read have been extremely valuable...." Read more

"...of the best in terms of explaining the theory in simple terms and very valuable from the perspective of the practical knowledge you may get from it...." Read more

"...Still easily worth the purchase price." Read more

15 customers mention "Math accuracy"7 positive8 negative

Customers have different views on the book's math accuracy. Some find it has complex math but explains things with basic concepts and minimal formulas. Others mention that the kindle edition has some faults in mathematical notations, typos in the examples, and missing function definitions.

"It's a good book and I'm enjoying it a lot but there are a few typos or missing function definitions in the code so you need to use the github..." Read more

"...a lot of the theory of ML in easy to understand language and simple math...." Read more

"...One caveat of the coding in this site is "coding may not be 100% correct"...." Read more

"...is discussed in detail with use cases and without drowning the reader in math proofs. Each piece of the code is discussed...." Read more

9 customers mention "Color scheme"3 positive6 negative

Customers have mixed opinions about the color scheme. Some say the pictures are colored compared to the paperback version, while others mention it's not in color, rendering some of the plots useless, and the paperback print is in black and white, making the material difficult to see in black and white.

"...I have brought book looking for colors of the graphs. There is no color in this book. I had the Kindle version already...." Read more

"...Pictures are colored compared to the paperback version...." Read more

"So, unlike the original book, this copy does not have colored pages except the cover. So it is not easy to interpret many graphs...." Read more

"...It would be perfect if print in color." Read more

9 customers mention "Figure missing"0 positive9 negative

Customers are unhappy with the lack of figures in the book. They mention missing images, graphs, and blank figures that make some figures useless.

"...the publisher prints in black and white making some of the figures in the print useless, but this is the case with all O'Reilly texts and I wouldn't..." Read more

"...This review is for Amazon/ O'Reilly.Many figures are missing in the book. I would like to have a separate copy...." Read more

"Great book, but marred by many missing figures. Someone sleeping on the job at O’Reilly." Read more

"Content is great, but a lot of images and graphs are missing." Read more

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Craig K
Practical and Engaging Introduction to Machine Learning
Reviewed in the United States on July 18, 2017
Verified Purchase

Hands-On Machine Learning strikes a perfect blend between application and theory. Beginners to machine learning will find it clear to follow and will be able to build complete systems within a few chapters while those with an intermediate level of experience will find a comprehensive, up-to-date guide to this exciting field.

Pros:
+ Practical: The book focuses on examples and implementations of the algorithms rather than the mathematics allowing readers to quickly build their own machine learning models
+ Readable: Geron does not get too caught up in the details, and he provides warnings when the next section is heavy on theory
+ Online Jupyter Notebooks: The Jupyter Notebooks that accompany this book (and can even be viewed for free with no purchase from the author's GitHub) are worth the entire purchase price. They feature examples of all the code in the book, plus additional explanatory material. The end-of-chapter solutions to the coding exercises are gradually being added to the notebooks.
+ Up-to-date: The leading edge of machine learning (and in particular deep learning) is constantly shifting, and Geron does his best to keep the notebooks updated. Multiple times I have read an ML paper and then found the technique implemented in the notebooks within weeks of the publication of the article. Some of the techniques in the book may not be at the absolute forefront of the field, but they are still good enough for learning the fundamentals.
+ Engaging: The book is a joy to read, and the author is quick to respond to issues pointed out by readers in the book or in the Jupyter Notebooks. Clearly, the author enjoys machine learning and teaching it to others.

Cons:
- Experts may find this book lacks enough depth because it is more focused on getting up and running rather than optimization. It also is specifically aimed towards Python (and Tensorflow for deep learning) so those looking for implementations in other frameworks will have to search elsewhere.
- Due to the rapidly-evolving nature of the field, a print book on machine learning will always need to be periodically re-issued to stay on top of all the developments. Nonetheless, the fundamentals covered in this book will remain relevant and the Jupyter Notebooks are constantly updated with new techniques.

Final Line: If you have some basic experience with Python (loops, conditionals, dictionaries, and especially Numpy) and zero to a medium level of experience with machine learning, this book is an optimal choice. I would recommend it both for those wishing to self-study and quickly develop working models, and for students in machine learning who want to learn the implementations of more theoretical coursework. I have enjoyed spending time working through the chapters and the exercises and have found this book extremely useful.

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Steven Marton
If I had to pick just one book to get me into machine learning, this would be it!
Reviewed in the United States on September 16, 2017
Verified Purchase
This has to be at the top of my list of most highly recommended books! The amount of material it covers is awesome, and I can find almost no fault with it. The writing is extremely clear, easy to read, written in impeccable English. Very well edited. I don't think I came across any spelling or grammar errors, or any real errors at all. Truly solid writing.

The breadth of information covered if quite wide. The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Even if you've read about these concepts a million times, you might still glean useful information from these pages.

The Tensorflow section is also super well done. Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.) to get you started. The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some. I also found that the notebooks show more examples than what's in the book, which can be nice.

I only went really hands on with the reinforcement learning notebook, and found that it was well done and a good base to start my own work from. Even just having a section on reinforcement learning is very rare in a book of this style, and Geron's samples and explanations are really solid. He obviously has a strong grasp of many varied fields within deep learning, and that includes reinforcement learning. The only thing I wish it had was an A3C sample, to make my life that much easier. But you can't have everything.

I really liked his tips on which types of layers, activations, regularization, etc. are most effective, and gives good starting points for decent convergence. His explanation of multi-GPU Tensorflow was also quite good. The Tensorboard section was also very useful.

In short, if you want ONE book to get you into machine learning, and Tensforlow is on your radar, you can't go wrong with this one. Highly recommended!

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sipy
A Powerful Tool for Hands-On Learning of "Machine Learning" in Python
Reviewed in the United States on April 10, 2018
Verified Purchase
A very well-written book that takes you beyond the "heavy curiosity" phase of your machine learning education. You need this book if you want to *understand*, from a critical perspective, how to accomplish things like - selecting features, culling data, creating provably-suitable ML models, model/data validation, and what it takes to actually get an ML platform to do something truly useful and meaningful to you!

In return for being so useful, the author requests something from you - get your hands on the keyboard, and actually work with Python Scikit-tools as well as the Jupyter workbooks that accompany the book (on github). You should have knowledge of Python, as many of the ML concepts are reinforced through concrete implementations in Python code. And work you must, as - after all - the book's title includes the words "Hands-On"!

I respect where the author is coming from, as he is trying to reduce his obvious experience in ML down to a "Hands-On" working environment. I believe he has accomplished this goal very admirably. His easy style of writing encourages you to try things for yourself, and removes the worry that you may "break something" along the way. What truly impresses me beyond even all of this is that English is not his first language! So - again, this work is very impressive on many fronts.

SIDE NOTE: This book will likely work for readers with both "step-by-step" and "random-access" learning approaches, as no topic appears to rely so heavily on the previous one(s) that it can't be understood on its own merits. Again - a very impressive feat, especially in light of the heady material being covered. Major Cudos by Mr. Géron!

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Douglas Ribas de Mattos
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Reviewed in Brazil on May 30, 2024
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Ruben
Indispensable para quienes buscan aprender Machine Learning.
Reviewed in Mexico on February 24, 2018
Verified Purchase
Excelente libro, para quienes están empezando y para quienes tienen cierta experiencia en este campo.

- Utiliza herramientas actuales y las librerías mas usadas.
- Aplicaciones reales con datos reales.
- Referencias a sitios web relacionados con el tema.
- Ejercicios muy interesantes y actuales.
- Conceptos muy bien explicados.

En lo personal poseo cierta experiencia en estos temas y no esperaba mucho de este libro, pero al tenerlo y empezar a leerlo me fascino, un libro mus imágenes.y bien hecho y se nota desde las primeras paginas que el autor es un experto en el tema, las herramientas y los ejemplos son muy y repito muy prácticos, fácilmente puedes replicar el código de ejemplo para tus necesidades y tus propias aplicaciones de ML.

Un Excelente libro, me atrevería a decir que de los mejores en la actualidad.
Altamente Recomendable.

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Miguel Angel Salinas Gancedo
Muy completo
Reviewed in Spain on October 10, 2019
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Para mi el mejor libro de Machine Learning, mu completo y con muy bueno ejemplos que van más haya de los típicos en otros libros.

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Mauri Claudio
A must-have book for any machine learning practitioner.
Reviewed in Italy on August 9, 2019
Verified Purchase
Excellent text. Covers both the theory and the practice of modern machine learning, providing the reader with a solid background , needed to tackle the matter with confidence.

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James W.
Great introduction, better than online resources I've used
Reviewed in the United Kingdom on February 17, 2019
Verified Purchase
Having just finished chapter 2, I'm finding this book goes into a lot of detail. Online courses I've taken go through a couple of steps to prepare the data, but this book really takes you through in much more depth. It shows you how to write custom transformers your data and how to make a pipeline and shows you how to evaluate your model as well. I'm really enjoying the book and I think anyone else who knows how to program already and wants to pick up machine learning should definitely pick up this book.

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition

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5.0 out of 5 stars Practical and Engaging Introduction to Machine Learning

5.0 out of 5 stars If I had to pick just one book to get me into machine learning, this would be it!

5.0 out of 5 stars A Powerful Tool for Hands-On Learning of "Machine Learning" in Python

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5.0 out of 5 stars N/A

5.0 out of 5 stars Indispensable para quienes buscan aprender Machine Learning.

5.0 out of 5 stars Muy completo

5.0 out of 5 stars A must-have book for any machine learning practitioner.

5.0 out of 5 stars Great introduction, better than online resources I've used

Practical and Engaging Introduction to Machine Learning

If I had to pick just one book to get me into machine learning, this would be it!

A Powerful Tool for Hands-On Learning of "Machine Learning" in Python

N/A

Indispensable para quienes buscan aprender Machine Learning.

Muy completo

A must-have book for any machine learning practitioner.

Great introduction, better than online resources I've used