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Hands-On Machine Learning with Scikit-Learn and TensorFlow

4.52  ·  Rating details ·  1,174 ratings  ·  91 reviews
A series of Deep Learning breakthroughs have boosted the whole field of machine learning over the last decade. Now that machine learning is thriving, 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 theor
Kindle Edition, 1st, 450 pages
Published April 9th 2017 by O'Reilly Media
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Priyansh Sharma i am not sure but what i know of is atleast ,u should have done quite a bit of programming and could understand python. it would be better if u have a…morei am not sure but what i know of is atleast ,u should have done quite a bit of programming and could understand python. it would be better if u have a grasp on linear algebra , calculus.(less)

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☘Misericordia☘ ⚡ϟ⚡⛈⚡☁ ❇️❤❣
A really nice and sensible intro to some of the most salient ML topics. Really visual and nifty in explanations, scikit/TF-oriented.

When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadlyTerminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s alreadyhere. In fact, it has been around for decades in some specialized applications, such as
Optical Character Recognition (OCR). But the first ML application that really b
Nov 04, 2017 rated it it was amazing  ·  review of another edition
One of the best ML books out there. Dives deep into the practical implementation of Sklearn and Tensorflow. Also, dives deep enough into the math side of ML. Read it from cover to cover. Really worth it.
Mihail Burduja
Jan 03, 2017 rated it it was amazing
The book contains a chapter that shows a basic flow for working with data problems. The TF chapters are interesting but somehow short. I would have liked more on convolutional layers and RNN.

The reinforcement learning chapter is very interesting.
Aug 06, 2017 rated it it was amazing  ·  review of another edition
great introduction into machine learning for both developer and non developers. authors suggests to just go through even if you don't understand math details.
main points are:
- extraction of field expert knowledge is very important. you should know which model will serve better for the given solution. luckily lot of models are available already from other scientists.
- training data is the most important part. the more you have it the better.
- so if you can you should accumulate as much data as
Jul 29, 2017 rated it really liked it
At the time of reading, I had already learned about most concepts in the book. So, I focused only on the deep parts of Tensorflow. It's a good book overall. I imagine it would be very useful for myself a few years ago.

My favorite part is the reinforce learning in the last chapter. The chapter makes sense, is easy to understand, and its example is very practical.
Aug 28, 2017 rated it it was amazing
This is the best book I've read on machine learning. It is well written and the examples are very good with real data sets.

The first half is an introduction to machine learning and the second half explores deep learning. It is a great book to read along an online course.
Lara Thompson
Jan 01, 2019 rated it it was amazing
Shelves: technical
A very excellent introduction to many machine learning algorithms beginning at the very beginning and ending much further than I expected. I can't wait for the updated edition to reference because, yes, many tensorflow functions changed name. ...more
Lukasz Pruski
Dec 27, 2020 rated it it was amazing
"Machine Learning is the science (and art) of programming computers so they can learn from data."

It is December 27th, four days until the end of the year, and I am four books short of my Goodreads 2020 Reading Challenge goal of 60 books. Never abandon hope! I will review two computer science books that were tremendously important to me and my students in 2020, books that helped me return to the field of neural networks and machine learning in general and helped my outstanding research student co
Aug 05, 2020 rated it it was amazing
Best ML book! It explains the most used ML concepts very well while being practical. The graphics used are amazing! It is a piece of art :)
Ferhat Culfaz
Aug 03, 2018 rated it really liked it  ·  review of another edition
5* for the first half of the book, scikit learn. 3* for the second half, Tensor Flow. Nice examples with Jupyter notebooks. Good mix of practical with theoretical. The scikit learn section is a great reference, nice detailed explanation with good references for further reading to deepen your knowledge. The tensor flow part is weaker as examples become more complex. Chollet’s book Deep Learning with Python, which uses Keras is much stronger, as the examples are easier to understand as Keras is a ...more
Fernando Flores
Mar 22, 2019 rated it really liked it  ·  review of another edition
Shelves: it
Nicely well explained from scratch to advanced
Shyam Poovaiah
Nov 08, 2020 rated it it was amazing
Shelves: programming
This is the first ML book I read.
It covers all topics along with practical examples.
This is a hands on book and there are no mathematical proofs.
The best book to start your journey in ML.
Consider the second version of this book since it uses Keras and TensorFlow.
Omri Har-shemesh
Sep 25, 2019 rated it it was amazing
Great book for introduction to machine learning using Scikit-Learn. I didn't like as much the part about Tensorflow but the scikit-leran one is great. ...more
Elie De Brauwer
Okay, best technical book read this year award (still three months to go though) goes to this book. Initially it feels a bit odd that the focus is first put on scikit learn and that tensorflow seems to be added as an afterthought but in the end it's really a right tool for the job approach. Don't use this book to learn scikit learn (or tensorflow) use this book to get your face wet in deep learning where the two libraries are just a tool. ...more
Mar 30, 2018 rated it it was amazing
Fantastic book to connect the dots between theory, techniques, and technologies in the ML world. The domain is moving fast, and this book---together with the source code---is to me a tremendous guide in the existing-yet-still-growing maze of approaches.

The book does assume being familiar with Python and Mathematics fundamentals, such as linear algebra. Armed with these two, I think the book strikes a really good balance between being didactic and concise.
Xiao Xiao
Jul 04, 2018 rated it really liked it
I like the first half of the book a lot. It provides a big-picture view of machine learning in general and some of the most commonly used algorithms, in a conceptual and easy-to-follow way. The second half, not so much. At least part of the blame lies in TensorFlow itself, which is still pretty much a moving target. So far the best resource I've encountered to learn TensorFlow is definitely still the Udemy online course. ...more
Kent Sibilev
May 29, 2019 rated it it was amazing
One of the best introductions to Scikit and TenserFlow frameworks. The book explains most of the concepts, but for the rigorous math treatment you should look for other books (for example, Hastie, Trevor. The Elements of Statistical Learning: Data Mining, Inference, and Prediction or Bishop. Pattern Recognition and Machine Learning). The fact that you can follow all the material using Jupiter notebooks was a nice touch.
Silver Meloman
Oct 06, 2019 rated it it was amazing
Shelves: 2019, cs-ai
This book is without any doubts the number one "hands-on" resource for anyone who wants to get into the field of Machine Learning.

Aurélien did an amazing job to cover a vast number of ML fields, from both theoretical/mathematical and practical/coding perspectives.

Definitely a must read for anyone who just started with ML or even for experienced folks. Second edition is even more exiting as it includes TF 2.0 and it's high level framework Keras.
Leandro Braga
Mar 21, 2018 rated it it was amazing
I couldn't recommend it more. This book delivers practical knowledge of machine learning with scikit-learn and tensor flow in a clear and concise text.

All the book is filled with examples and also extra solved exercises which really help you to grasp the content.

I would recommend it for anyone who is trying to use machine learning in a project.
Aug 17, 2017 rated it it was amazing
Excellent introduction to Machine Learning and Neural Nets. Some of the code snippets in the book are lacking, but full examples are available on the authors git repository that clear up any questions, and often go further in depth.
Sachin Date
Dec 31, 2017 rated it it was amazing  ·  review of another edition
An easy to understand book on machine learning

Read this book _before_ you pick up one of the ' standard' texts on the subject such as Deep Learning by Goodfellow et al.
To make the most of this book, be sure to first brush up on Python and Linear Algebra.
C.P. Mulé
Feb 13, 2018 rated it really liked it
Great book, just not a great book for the purposes of an introduction to ML. In order to really appreciate what this book has to offer, A. Plan to do the exercises, & B. Know your mathematics better than I did when reading it.
Chai Zheng Xin
Sep 13, 2018 rated it it was amazing  ·  review of another edition
Super useful book for a beginner to understand machine learning and get started on projects. I took a graduate level course on Machine Learning and yet still learnt many practical knowledge from this book.
Dec 17, 2019 rated it it was amazing
One of the best ML books out there. It dives deep into the practical implementation of scikit-learn and tensorflow. Also, dives deep enough into the math side of ML. Read it from cover to cover. Really worth it.
Daniel Mendelevitch
Mar 11, 2019 rated it it was amazing
This book does a really good job at teaching fundamental ideas, concepts, and skills within the machine learning field. Furthermore, while some of the "hands-on" portion of it is outdated, a large majority of the implementation is very well-done and is very clearly explained. The book covers a wide variety of topics such as linear regression, ensemble learning, and deep RL, but makes sure to include some of the basic underlying principles behind ML.

My favorite thing about this book was the leve
Hồ Vinh
Aug 18, 2018 rated it it was amazing
Shelves: 2018
Here are what I expected from the book and it actually did achieve:
- The intended way to use Scikit-learn and TensorFlow. Specifically, essential building components, the understanding of their capacities in modelling and how to extend a model systematically, from a software engineering's perspective.

What I did not expect but happy to learn about:
- A typical guideline of how to attack a machine learning problem.
- An update of almost all well-known models: the first half is about Decision Tree, S
Olatomiwa Bifarin
Oct 18, 2020 rated it really liked it  ·  review of another edition
This will go straight to the top of my list of best ML books. Its minimalist on theory and largely a code repository, hence Hands-On. This is the first edition which is an important point to make, I got the first edition read 3/4 of it's content only to realize that a second edition had been published, and that I wasn't getting the 'full package', so I stopped and got a 2nd edition (... still ploughing through). A few changes to note in the 2nd edition. 1) Additional ML topics were covered such ...more
Kursad Albayraktaroglu
Reading this book and completing all the programming exercises was a significant effort; but I feel that it was totally worth it. While it most definitely requires a strong background in programming, Geron's book is a very thorough and approachable text for learning TensorFlow and machine learning.

Some of the concepts covered in the book (reinforcement learning etc.) are deep enough to require dedicated books; and their coverage in the book was necessarily a bit light. In these cases, the aut
John Giumanca
Oct 28, 2019 rated it it was amazing  ·  review of another edition
This book is amazing. I am on my first steps into artificial inteligence and I need to say that in this book I found very good explications to many subjects I had problems understanding. This book is an introduction to a bunch of topics regarding machine learning with hands on examples. Maybe one of the main strengths of this book is the hands on approach. It shows you how to implement certain structures and algorithms to get a better insight on how they work. I am also buying this book as paper ...more
May 25, 2020 rated it really liked it
This is an exceptionally poignant introduction to applied machine learning. The "hands-on" in the title is certainly on point. The author covers sufficient theory and mathematics followed by implementation using relevant and state of the art tools. He has repositories that can help beginners bridge the gap between theory and practice. The prose is crisp and not verbose. The examples and back exercises are helpful in building comfort with the programming side of things.

I strongly recommend this
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