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

Fantastic content with valuable examples.

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!

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!

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.

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.

Super fast and in good shape

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!

I like the book and it helps me to get the fundamental stuff.