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

Hands-On Machine Learning strikes a perfect blend between application and theory. Beginners to machine learning will find it clear to follow and will be able to build complete systems within a few chapters while those with an intermediate level of experience will find a comprehensive, up-to-date guide to this exciting field. Pros: + Practical: The book focuses on examples and implementations of the algorithms rather than the mathematics allowing readers to quickly build their own machine learning models + Readable: Geron does not get too caught up in the details, and he provides warnings when the next section is heavy on theory + Online Jupyter Notebooks: The Jupyter Notebooks that accompany this book (and can even be viewed for free with no purchase from the author's GitHub) are worth the entire purchase price. They feature examples of all the code in the book, plus additional explanatory material. The end-of-chapter solutions to the coding exercises are gradually being added to the notebooks. + Up-to-date: The leading edge of machine learning (and in particular deep learning) is constantly shifting, and Geron does his best to keep the notebooks updated. Multiple times I have read an ML paper and then found the technique implemented in the notebooks within weeks of the publication of the article. Some of the techniques in the book may not be at the absolute forefront of the field, but they are still good enough for learning the fundamentals. + Engaging: The book is a joy to read, and the author is quick to respond to issues pointed out by readers in the book or in the Jupyter Notebooks. Clearly, the author enjoys machine learning and teaching it to others. Cons: - Experts may find this book lacks enough depth because it is more focused on getting up and running rather than optimization. It also is specifically aimed towards Python (and Tensorflow for deep learning) so those looking for implementations in other frameworks will have to search elsewhere. - Due to the rapidly-evolving nature of the field, a print book on machine learning will always need to be periodically re-issued to stay on top of all the developments. Nonetheless, the fundamentals covered in this book will remain relevant and the Jupyter Notebooks are constantly updated with new techniques. Final Line: If you have some basic experience with Python (loops, conditionals, dictionaries, and especially Numpy) and zero to a medium level of experience with machine learning, this book is an optimal choice. I would recommend it both for those wishing to self-study and quickly develop working models, and for students in machine learning who want to learn the implementations of more theoretical coursework. I have enjoyed spending time working through the chapters and the exercises and have found this book extremely useful.

This has to be at the top of my list of most highly recommended books! The amount of material it covers is awesome, and I can find almost no fault with it. The writing is extremely clear, easy to read, written in impeccable English. Very well edited. I don't think I came across any spelling or grammar errors, or any real errors at all. Truly solid writing. The breadth of information covered if quite wide. The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Even if you've read about these concepts a million times, you might still glean useful information from these pages. The Tensorflow section is also super well done. Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.) to get you started. The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some. I also found that the notebooks show more examples than what's in the book, which can be nice. I only went really hands on with the reinforcement learning notebook, and found that it was well done and a good base to start my own work from. Even just having a section on reinforcement learning is very rare in a book of this style, and Geron's samples and explanations are really solid. He obviously has a strong grasp of many varied fields within deep learning, and that includes reinforcement learning. The only thing I wish it had was an A3C sample, to make my life that much easier. But you can't have everything. I really liked his tips on which types of layers, activations, regularization, etc. are most effective, and gives good starting points for decent convergence. His explanation of multi-GPU Tensorflow was also quite good. The Tensorboard section was also very useful. In short, if you want ONE book to get you into machine learning, and Tensforlow is on your radar, you can't go wrong with this one. Highly recommended!

This is one of the most accessible machine learning books out there for developers. It strikes a nice balance between intuition, mathematical details and implementation specifics. I would highly recommend this book to beginners and intermediate ML practitioners. I'm only giving it four stars because despite the content itself being great, the print does have some issues like missing diagrams (see attached pictures).

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Dieses Buch ist bei weitem das beste Buch, um Machine Learning mit TensorFlow V1.x zu erlernen. Mittlerweile wird hauptsächlich PyTorch statt TensorFlow verwendet. Und das entsprechende Buch – "Hands-On Machine Learning with Scikit-Learn and PyTorch" [1] – wird noch dieses Jahr erhältlich sein. [1] https://www.amazon.de/Hands-Machine-Learning-Scikit-Learn-PyTorch/dp/B0F2SG98Q9

Having just finished chapter 2, I'm finding this book goes into a lot of detail. Online courses I've taken go through a couple of steps to prepare the data, but this book really takes you through in much more depth. It shows you how to write custom transformers your data and how to make a pipeline and shows you how to evaluate your model as well. I'm really enjoying the book and I think anyone else who knows how to program already and wants to pick up machine learning should definitely pick up this book.

I bought a few other machine learning books before, and this one is by far the best. It is very thorough, and extremely clear. It covers everything I was hoping to learn: convolutional neural networks, deep reinforcement learning, recurrent nets, and it clarified a lot of things I thought I already knew: random forests, ensemble learning, svms and so on. There's a ton of great figures and graphs, it's easy to read and the author is clearly knowledgeable. I like the fact that there's pointers to the original papers everywhere. All the code examples are on github, and there are many exercises (I only did the tensorflow ones, but they were great). Very "hands on", like the title says.

Excelente libro, para quienes están empezando y para quienes tienen cierta experiencia en este campo. - Utiliza herramientas actuales y las librerías mas usadas. - Aplicaciones reales con datos reales. - Referencias a sitios web relacionados con el tema. - Ejercicios muy interesantes y actuales. - Conceptos muy bien explicados. En lo personal poseo cierta experiencia en estos temas y no esperaba mucho de este libro, pero al tenerlo y empezar a leerlo me fascino, un libro mus imágenes.y bien hecho y se nota desde las primeras paginas que el autor es un experto en el tema, las herramientas y los ejemplos son muy y repito muy prácticos, fácilmente puedes replicar el código de ejemplo para tus necesidades y tus propias aplicaciones de ML. Un Excelente libro, me atrevería a decir que de los mejores en la actualidad. Altamente Recomendable.