Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition

I have just finished Hands-On ML book and I cannot recommend it enough.I have been working as a Mobile Software Developer for 12 years and now I am thinking about trying something new. I remember some Math and Statistics from school but definitely not enough to get deep into the subject.From my experience, you can read the book and finish all the exercises without understanding any of the Math (although as author points out, it is beneficial if you understand the Math behind it - e.g. to understand why it works, read and implement papers).Book goes into the detail and explains the history of how we got there so it was fairly easy for me to follow and understand majority of the book. I missed this kind of detail from ML courses that I tried. You will also see significant papers explained - something that would be difficult for me to do alone at this point.However, one thing I appreciated the most were the exercises. In ML courses I tried, the exercises were simple and too easy to give you anything. Here it was a real challenge and I have a good feeling about what I learned by doing those exercises.There are also a lot of references for books or papers in case you want to focus on a specific area.One blind spot I am seeing though is focus on Keras/TensorFlow and GCP pipeline whereas the most examples on internet seem to be from PyTorch and AWS as a most popular cloud solution. However, as author points out, if you know one it will be easy for you to switch (I also reimplemented some of the PyTorch projects as part of exercises without too much difficulty). Still, I need to think about it and get some more PyTorch and AWS experience.

It's a very good introductory book to supervised and unsupervised learning algorithms. It has a lot of code and brief explanations of the theory. It's a very good start if you want to venture into the world of machine learning.

Excellence content and print quality, strong fundamentals to understand machine learning concepts with practice

This book is the best one I have been looking for in my career development. I appreciate your quick service delivery, and I thank the Author for the tough work they have done with us.However, the cost of transport is so challenging (from the AMAZON/USA to Africa/Ghana is so expensive), and the book cover page would be a hard one instead of a paper cover, as the book is heavier.Thank you.

Amazon packages the book inside a box without any inner cushioning or envelope. It arrived with a bent corner and tiny bruises. That aside the book is ok.

Wow! What a thorough and well written book. It starts out with examples if you are purely interested in how to apply ML methods. The rest of the book takes a deeper dive into what the different algorithms are doing and gives examples of how to apply each method. I think the level of the writing is a great balance between thoroughness and approachability. My background is in mechanical engineering and I find the detail of the book to be satisfying, without getting so bogged down in theory and proofs as to make it overwhelming. It is also very comprehensive - you could use this book as a reference for looking up more detail about specific algorithms as they come up in your work/learning, but I am actually enjoying reading it cover-to-cover to broaden my understanding of the subject.Finally, you can't beat the price for a book of this quality. This well exceeded my expectations.

great and well written

I dislike it when people complain about a damaged box. At least they got their box. About 20% of my Prime deliveries are dropped off at some other residence. In the future, I'll just mark it "not received".As for the book, it covers what I need at just the right level. It assumes the reader is naive to AI (as I am). I can prompt fine, but I need deeper coverage. I was most interested in TensorFlow and Keras. Covering Scikit-Learn is a bonus.