HANDS ON MACHINE LEARNING WITH SCIKIT-LEARN & TENSORFLOW: Concepts, Tools, and Techniques to Build Intelligent Systems Paperback – 1 January 2017

This book takes a different approach to teaching ML. Instead of focusing on just concepts, practical hands-on experience is given more priority. Here are some of the pros and cons:Pros:1. Great laid out content.2. Example-based approach.3. Lots of projects to do.4. Teaches how to build a ML model instead of focusing on the underlying concepts only.Cons:1. Little short of concepts, actually.This book serves a great deal. But still you should know about Py-dependencies like NumPy, Pandas, Matplotlib etc. which are explained in O'Reilly's Data Science Handbook quite well.To learn python, I would suggest going for free resources at Udacity, CodeAcademy or even some of O'Reilly's free eBooks.Sometimes you might not understand a particular implementation. In those cases having a little conceptual background helps. I suggest going for Ian Goodfellow's Deep Learning book which can be grabbed for nothing from GitHub. This book explains the concepts immensely well.Thank You.

Tha book is all what one needs to be confident to pursue analytics journey.Having spent years in the analytics industry, I find the book good for a person with some elementary know how of Machine learning like regression, Decision Trees.Part 1 of the book is good for beginners to make their knowledge concrete on the basiscs of Machine learning algos.Part 2 is more advanced stuff and talks about Neural nets ( different types) and dee learning.One gets to do analysis on datasets with codes , to get the right feel of an analytics project.A good book for anyone looking to get ahead..

This book is probably the best introduction for Machine Learning frameworks for some looking to apply it in their daily work or just as a hobby. Its not an academic textbook at all as focus on proofs and theory is left for exploration. Its mostly a guided tour with important things to remember about each ML algorithm.The addition of exercises at the end of each chapter is a welcome feature as it really tests your understanding. If you are familiar with Python then this is probably the first ML book to learn. Good luck!NOTE ON INDIAN EDITION: The printing quality is abysmal and really disappointing. Color printing would have been very useful as most of the charts are comparisons and would help in visualizing tuning of hyperparameters etc. Get the US edition if you can spare the change.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent SystemsThis is one of the best books you can get for someone who is just starting out in ML, in its libraries such as Tensorflow, It covers the basics very good. As a book, it is 5/5Once you are done with this book, the ideal next step is the "Deep Learning Book By Ian Goodfellow".Sadly my copy didn't look so good, If it were an under 300 book, I would have let it slide but when the book costs 1450 (Which it is totally worth it) I expected a much better copy.

Quick glance shows that subject covered is done with just the right amount of focus on basics vs hands-on ML.Quite simply written and not intimidating at all. For those looking for a very deep look into the basics and the math background of the concepts should probably check out Duke University’s machine learning mastery with excel - which is a rigorous crash course on the very basics of the math.The problem with book quality on amazon is hit or miss. Paper that it’s published on is slightly cheap quality.Looks like also someone has used the book. That may be a concern to some people.

Of course I've got great feedback about the author and I totally agree with them. The book is worth the investment. And for the quality, I am satisfied with the copy received. Print and page quality is good. And fantastic work by Amazon as usual.

I would recommend the users to wait for second edition of this book. The Keras has become default API for Tensorflow 2 and many features like placeholders , initializers , variables etc have become redundant. However this book is a masterpiece because it teaches you lot of practical applications with a substantial theory.

By far the most complete and accurate hands-on book on machine learning and deep learning. Author has done a remarkable job in giving details in just the right amount. No over-doing or under-doing in this one. Code given in the jupyter notebooks works like a charm and covers almost everything. Highly Recommended!!