Hands–On Machine Learning with Scikit–Learn and TensorFlow

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems This 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.

Example/code presented in the book is not compatible with latest release of the tensorflow. Reader will have to make the program work after lot of debugging and searching on net, hence can be sometimes very frustrating. Started with few chapters, but had to leave it in the middle because of this issue. But serves as a good starting point in terms of theoretical aspects on neural networks (cnn, rnn).At the same time I was unable to find a book dedicated on deep learning with tensorflow. Not a bad book at all, but incompatible with latest version of tensorflow. Can be used as a reference for learning/ understanding cnns, rnn etc.

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.

Poor publication for Indians. Graphs are printed in Black and White making it quire hard to make observations. Packing was poor as well. Not worthy of Rs. 1500. Try to get the original one and one which is published by SPD. Really very disappointed.

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.

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 is a black and white print. Looks very much like a photocopy/xerox. The images shown on the website does not match what I received - different barcode at the back of the book. On the O'REILLY website, this ISBN number is a color print. I bought this assuming it is the original color print imported from O'REILLY, USA. (To really appreciate the graphics,charts,graphs,plots, etc, you need to view in color). But this book does not look like original. Paying Rs. 3309 is worthless for this black and white print. You get SPD publication of the same book for Indian continent for Rs 1400.

The book has printing mistakes where basic linear equation was printed wrongly.. Also print is below the standards with black and white graphical images. Very dangerous to buy for ML enthusiasts.. Better buy the orginal copy..in the image I uploaded the theta 0 is not used in the formula..

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 simple layer over tensor flow to ease the use. Also Chollet explains the concepts better and nicely annotates his code.Buy this book for scikit learn and overall best practise for machine learning and data science.Buy Chollet’s Deep Learning using Python for practical deep learning itself.Overall still a practical book with Jupyter Notebook supplementary material.

The table of contents is missing in the Kindle preview.THE FUNDAMENTALS OF MACHINE LEARNING1. The Machine Learning Landscape (comment: probably the most lucid ML explanation I've ever read)2. End-to-End Machine Learning Project3. Classification4. Training Models5. Support Vector Machines6. Decision Trees7. Ensemble Learning and Random Forests8. Dimensionality ReductionNEURAL NETWORKS AND DEEP LEARNING9. Up and Running with TensorFlow10. Introduction to Artificial Neural Networks11. Training Deep Neural Nets12. Distributing TensorFlow Across Devices and Servers13. Convolutional Neural Networks14. Recurrent Neural Networks15. Autoencoders16. Reinforcement Learning

Amazing book. I would just like to point out that the description for the kindle edition carries the disclaimer (in bold) that "Graphics in this book are printed in black and white". This is not true, they are very much in colour and this makes a huge positive difference, especially for graphical information presented in multiple dimensions.As an enthusiastic hobbyist, some of the descriptions of what is "under the hood" were slightly beyond my ability to fully comprehend. However, the book is so well-written that this becomes inspiring rather than frustrating. So my next project is to improve my math.

This book is a fantastic introduction to TensorFlow and pretty modern neural network techniques. I was a little worried buying this book that it would focus too much on Scikit Learn, but this is not the case. This book is approximately 50:50 Scikit and TensorFlow.I bought this book as I was using TensorFlow and neural networks for my Masters Thesis and it delivered exactly what I needed to kick start my research. A pretty concise summary of some methods and what to use to get started. It is an easy read and can be consumed pretty fast if you are even vaguely familiar with the underlying theoryI wish I had more hands so I could give this book three thumbs up.

I have started reading, just finished a few chapters. As of now I really like the chapter formats, introductory chapter, covering entire ML start to end.The 2nd part of the book is where in all the fun kicks in, but that would be bit difficult if you havent read the first part of the book.The foundations explained in the 1st half of the book are really usful for understanding the 2nd half of the book.That being even though I like tensor flow, it can be a bit difficult thinking about creating the graph and then executing it separately, it isnt that intuitive (well at least for me), I have also started to look into Keras which seems to be much easier to just get going, I was debating between TFLear and Keras as a highlevel API over TensorFlow but I seemed to like Keras - I guess it has a larger community.