Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition (Full Colour) Paperback – 23 October 2019

UPDATE: A revised, third edition of the book is releasing on 31st of October 2022. Would definitely recommend waiting for it rather than buying this second edition at this point.I had my eye on this since the author published the latest version earlier this year however I was skeptical regarding Shroff Publishers faithfully reproducing it and that too a full colour edition. Fortunately I was proven wrong as both, the paper quality itself and other aspects like font and coloured images were on point and precise.Regarding the content itself, this revised version of the book has been updated for Tensorflow 2.0 and latest version of the Keras API.As described, the book while ensuring it delivers a brief introduction behind the technical components of a machine learning project lifecycle and ideas; focuses primarily on the working of the tools and their respective capabilities with code snippets for most of the examples present explained throughout the book.Notebooks containing source code for all the described examples and case studies, are present on the authors public Github repository under 'handson-ml2'. Also includes the datasets utilized some of which are modified versions of other publicly available datasets.Do note that environment setup described is as according to a Linux and Mac based terminal. For Windows just like the author himself notes, I would highly suggest installing Anaconda (or Miniconda - lightweight bare bones installation of the same which I personally recommend and use). Once you have made a virtual environment and installed the required packages using conda package manager you can follow all the rest of the instructions in the book.Overall this handbook is highly recommended for anyone who is decently versed with Python and has a rough idea about what Machine Learning actually is because unfortunately there are a lot of buzzwords in the market involving the field of machine learning and data science, the latter of which is a much broader term.So if you are just starting out to explore this area I suggest beginning with some other resources which define and clarify some of the myths as you should know what you are actually getting into before fully investing your time. For learning the basics of Python itself, I personally suggest 'Byte of Python' which is a freely available ebook for a brief introduction to the language.

It is a essential book on machine learning.

If you are a practitioner of ML, I'd suggest you to buy this book with eyes closed. The author doesn't go extremely deep into the theory behind most of the algorithms, because well... that's not the primary focus of this book. It's more hands-on, as the title suggests, and I think the contents justify that pretty well. For a more comprehensive treatment to the theory behind the algorithms, I'd suggest to go with 'The Elements of Statistical Learning' or 'Bayesian Reasoning and Machine Learning'. Both are freely available online.I had the 1st edition of this book but still chose to buy this new and improved iteration mostly because of the following reasons.1. Addition of new materials (e.g. more unsupervised learning, more deep net techniques, new CNN architectures, etc.)2. Migration of codebase to Tensorflow 2.0.3. Discussion on Training and Deployment of Tensorflow models at scale.This is a colored edition, unlike the 1st edition of the book. It helps immensely, especially for the visualizations and graphs. The page quality is very nice too, and they have a glossy finish.

Contains almost all the relevant topics and important paper highlights before 2019, but in case of NLP there have been much development after 2019 like BERT. So, a new and updated version will be better compared to this one. Overall worth the money.

I got this book yesterday and I already finished two chapters. Here is a short summary of the content:- This is a very practical introduction to machine learning using Python.- I have some experience with ML concepts. Although the book says it's for beginners, I still find it helpful. The second chapter, end-to-end machine learning projects, helped me to understand a few small yet important steps we tend to miss/ignore when we work on a large scale ML project.- Choose this book if you know Python. Even if you don't have Python experience, with a crash course, you can easily learn Python and the concepts in the book.Now a little bit about the book: The book is of high quality! It is also a bit heavy due to the quality of the paper. All the pages are colored and glossy and attractive enough to make you wanna read the book!I recommend this to anyone with no/beginner/intermediate experience in ML.

The paper quality, colors and the binding is really top notch! The paper is matte(as it should be) and not glossy as mentioned in previous reviews, which is perfect for reading (Got the 4th Indian Reprint edition : November 2021).No complaints on the book, but a complimentary bookmark would have made it better.

This is the best I have ever read in my life! The delivery was good and the product is in excellent condition. This book is definitely recommended to people starting out with Machine Learning, as it covers A-Z of data science from beginner-level topics up to wayyy advance topics of ML, DL, and NLP. It's totally worth it!

So Finally I got the book at around 2000Rs. The other one was at 5000. I was confused about what is the difference between both, will this one be with tensorflow2x or it is the old one.This one is genuine and good quality colored printed pages. Covering all the topics and basics for Deep Learning and Machine learning. I have an iPad for reading books but I am more comfortable with folding pages and writing on paper rather than thinking about the battery when I need to study. So bought this one and looking forward to completing it.Costly but yeah worth being an OReilly Book.Thanks for reading.