Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

I've been following this book since its first edition, about time I write a review! It really does strike the perfect balance between code and theory. Everything is clear and written in a friendly tone. It'll get you started in applying everything from basic linear regression through decision tree, all the way to deep learning. My favorite is chapter 2, which is a step-by-step guide on exploring a data project, it's like having a professional guide you. I'm an experienced software developer, and I owe this book a lot for introducing me to many concepts. I'm old-school, so sitting down with a book and copying code examples takes me back and is a familiar experience. For some people, copy pasting might be more intuitive but you really can learn from doing things by hand. The full code is on github, but I recommend using it for reference only. What this book isn't, and doesn't pretend to be, is an introduction to Python. Some basic programming knowledge is needed, but if you want to work in the field, you'd need that anyway, and you shouldn't be afraid to dive into it. Looks like I'll be checking the 3rd edition!

Fantastic content with valuable examples.

I'm currently getting my MS in health data science and this was the book we had to get for my machine learning class. I was annoyed when the teacher said the class would be textbook heavy and he was only going lecture on high level concepts, I thought there was no way textbook would be able to a carry a class and boy was I wrong. This is hands down the best textbook I've ever bought! I never expected a data science text book to be easy to read but this book flows so well!, its easily digestible and it gives great examples with data that is easily available. You can write completely functional ML code from this book alone but one of the best features is that the book has GitHub site broken down chapter by chapter that helps fill the code out. If you are someone like me who hadn't had any experience with Matplotlib the github was super helpful because it covers in depth how to make really nice plots for the various models. I would recommend this book to anyone who is doing machine learning. The only thing I would change about this book is when it gets into decision trees, RF, various boosting types, XGB, as it moves through the models it only gives an example of the classification form of the model or the regression for of the model and I think it would be helpful if it gave examples for both for each model. But with that being said this was a pretty minimal thing I would change and I would still buy the book again even if they didn't change it! It's definitely worth the money!

I took a machine learning graduate course in my master program. I had a top conference paper. The professor used 1st edition of this book as one textbook for the course. I had a 1st edition of the book but did not have time to read. Now I buy the 2nd edition because the Tensorflow 2 has merged with Keras, which means we can avoid to learn the hard syntax of tensorflow 1.0, and there are a lot of new advances in machine learning, such as generative models. Also to my surprise, the book is colorful. That makes the book is more interesting.Each chapter has summary of math. That is better than some programming machine learning books that do not have any math. If you have some backgrounds in math of machine learning, this book can save you time because it gives you the whole picture without lost. If you are very interested in some equations and want to derive them, you can use Pattern Recognition and Machine Learning book.The Github has a lot of python projects of machine learning. The codes are well-written. If you can write codes like the codes in the projects, you will have the potential to enter Google.Go Google, the book is a must have.

One of the best written deep learning books I’ve ever read. Simple to follow. Easy language even for beginners.A four star because at the time of purchase seller quoted a discount on brand new copy but I ended up receiving a used copy. Second time this has happened to me on this platform.

I am only about to start with chapter 4 but if the rest of the book is of the same quality as the first few chapters then it definitely deserves 5 stars. The title of the book covers the content, and the book comes loaded with practical advice as well as working code samples. In fact is comes with complete projects in the form of Jupyter Notebooks. You really cannot go wrong buying this book, certainly given the price. Even if there are some chapters you end up liking less then it's still worth the money.One heads up is that it's not an easy read. That is partly because of the nature of the material, and partly because the author thankfully goes into the technical details of the what and how (and does so in a very accessible way). There is no "handwaving"! As a result the text is a bit dense and it can make for slow reading, but on the other hand it then leaves you with the satisfaction of a rather good understanding of the topic.One more thing - it probably does not hurt to be well versed in Pandas, especially matrix-wide operations in a single line of code.

I'm very pleased with this book. I enjoy the little bits of humor here and there, and it does a great job not glossing over important details that might be a stumbling block for someone. I'm quite comfortable with python however I appreciated that he did go into depth on setting up virtual environments and best practices. I remember years back when I was starting that whole concept tripped me up so much, having this explained so well is going to save someone a lot of time. Also his code seems so far to be written in a very thoughtful way and has them all on github. He also goes into lots of gotchas and tips and tricks that just overall seem to add a certain maturity to his writing. He has obviously very well versed in machine learning.Overall I would recommend. It's been much more interesting than I expected.

This book is amazing