Review of Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow

[Hujun]

Conclusion at first, this is the BEST book at the current moment (2019) about machine learning engineering practice, not only for the beginners but also for the veterans. Though the edition I read is only the review edition, it is still so impressing. I will buy a formal edition without any hesitation when it is released.

As we all know, the most important part in learning computer science and machine learning is practice. It is also the hardest part, especially for the beginners. The beginners mentioned here are not the green hands who know nothing about CS or ML theories. It would be sometimes also very difficult for the experienced developers and researchers when they begin to get into a new topic, or try to use a new framework, because it usually concerns a lot of tricks, nouns and existing unfamiliar patterns. It's why there are some many books with "hands-on" and "in practice" in title. But at the same time, not many of them are worthy reading. Obviously, Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow is one of the real masterpieces among these "hands-on" series.

This 2nd edition inherits most of contents from its previous edition (Hands-on Machine Learning with Sklearn & Tensorflow). You can find all the differences info in the preface of the book. It is very helpful for the readers who have already got the 1st edition. All code from the book is hosted on github. The author is continuously updating and patching the code. For the experienced machine learning veterans who only care about practical skills such as parameter tuning, model selection, framework usage etc., these up to date Jupyter Notebook code is true treasure. All details are there. You can have a brief conception of how modern ML works no matter how little you know about ML & DL theories. Compared to this book, more than 90% of the other "hands-on" book are just trash for completeness and originality. Author also gives many hints and easily understanding explanation in the book. I have to say these suggestions are highly valuable for pitfall avoiding. I guess that many people of you would have the same feeling as me, just because you have spent so much time in exhausting bug fixing while playing with sklearn, keras and tensorflow. Considering the saving of time, this book is just too cheap!

Of course, this book is not a good teaching material for machine learning & deep learning theory details. If you want to have deeper and more details about the theory, other classic books are no doubt much better, like Deep learning by Ian Goodfellow, Machine Learning by ZHOU Zhihua and so on. Personally, I recommend to reference these materials each other when you read them. A effective learning is always a spiral circle between theory and practice.

[arastogi1111]

Great review. I bought this book recently and even though I've done ML at a deep theoretical level, I'm enjoying this book (starting chapters atleast). How would you rate the Tensorflow+Keras materials in this book? Is the part2 (DL) of the book still the best? What about Chollet's book?

Also, please suggest a similar or the best possible book/resource for PyTorch, starting from basics to advanced. Thank You very much.