Hands-On Machine Learning With Scikit-Learn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems

Well written and organized book, the book starts with learning by doing is the best approach in chapter 2. However, you may need to search for extra resources for Math and python, still it is fine.

One of the best books I read for ML and Deep learning, explains the concepts clearly and provide Code with line by line explanation.

Livro excelente e muito bem didático.

This book should be regarded as a "gold-standard" for technical books. It balances theory and practice, has exercises (actually with answers!) and covers a tremendous breadth and depth. The book starts out in a refreshingly unconventional way of giving you a crash course in ML concepts before diving in to an end-to-end project. I note that one reviewer didn't like that but I liked it a lot. While a lot of it will go over your head if you lack experience (and the author assumes you don't have much), it gives you appreciation of what an overall real-life project might look like. The rest of the book is spent unpacking each of those stages. The first part of the book looks at more "classical" or traditional machine learning concepts like linear regression, logistic regression, SVMs, decision trees, ensemble learning and unsupervised models. Along the way you learn a lot of data science best-practises and how to train and test things properly. The second part dives into deep learning, progressing from general neural networks to CNNs, RNNs, LSTMs, autoencoders and GANs. You get a flavour of how GPT models work. Other topics covered in this section are Tensorflow and Keras (including a part on deploying models) and a chapter on another paradigm: reinforcement learning. Geron doesn't shy away from the math but gives you enough theory to appreciate the detail if you like that, and explains it in intuitive ways and with code. Some of the formulas can look intimidating but they are unpacked and explained well. There are review questions and/or exercises at the end of each chapter. One of my biggest frustrations with technical books in general is when they give you questions but no answers. Here, you get answers and also worked code in the provided notebooks, which is amazing. Other technical authors: take note. The exercises are often quite challenging to implement or at least open-ended, but I believe that to be a good thing. I learnt a lot from doing them (I'll admit I didn't do all of them!). The writing is clear, engaging and often humourous. To sum up, if you want to learn more about ML, I highly recommend this book. This review is for the 2nd edition but I'll be buying the 3rd edition and will definitely be re-reading. There is so much great information to take in. Thanks to the author for this masterpiece.

Excellent book for getting into machine learning. Plenty of example code.

Good Packaging done. Great Job.

This second edition book is totally worth your money