Hands–On Machine Learning with Scikit–Learn and TensorFlow

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.

This book is a fantastic introduction to TensorFlow and pretty modern neural network techniques. I was a little worried buying this book that it would focus too much on Scikit Learn, but this is not the case. This book is approximately 50:50 Scikit and TensorFlow. I bought this book as I was using TensorFlow and neural networks for my Masters Thesis and it delivered exactly what I needed to kick start my research. A pretty concise summary of some methods and what to use to get started. It is an easy read and can be consumed pretty fast if you are even vaguely familiar with the underlying theory I wish I had more hands so I could give this book three thumbs up.

5* for the first half of the book, scikit learn. 3* for the second half, Tensor Flow. Nice examples with Jupyter notebooks. Good mix of practical with theoretical. The scikit learn section is a great reference, nice detailed explanation with good references for further reading to deepen your knowledge. The tensor flow part is weaker as examples become more complex. Chollet’s book Deep Learning with Python, which uses Keras is much stronger, as the examples are easier to understand as Keras is a simple layer over tensor flow to ease the use. Also Chollet explains the concepts better and nicely annotates his code. Buy this book for scikit learn and overall best practise for machine learning and data science. Buy Chollet’s Deep Learning using Python for practical deep learning itself. Overall still a practical book with Jupyter Notebook supplementary material.

Overall, it's an excellent book for both theoritical and practical. The theoratical part is easy to understand and the author let's you understand the material smoothly. Usually, books like this make you sleep, but this book stands out. Good job for the author.

Pretty good explanation of several aspects of Machine Learning. The author goes into a good deal of the mathematical background despite it being a practical book. e.g. I finally learned the step between quadratic programming (from convex optimisation) and SVMs. That said this is not an introductory book. You are expected to know Python and a good deal of the data libraries beforehand.

Covers everything from simple linear models, SVM, random forests right through to modern neural nets/deep learning: CNNs, RNN and reinforcement learning. The writing is excellent. It seemed like every time I wondered something the answer was in the next paragraph. The pace was perfect for me though I have done some of this before - I wonder if it moves too fast for some.

I have been studying AI on and off for over 25 years, and have worked on statistical modelling for the past 20 years, including at Caltech and in Wall Street. I am currently running a summer program on Machine Learning in finance at UCL and am writing positions papers on ML, AI and big data. I own a library of Mathematical Statistics, Modelling, AI, Pattern Recognition, Machine Learning, Python, R, etc books and I have to say that this book makes all the others redundant. This is like Wilmott/Hull is for finance, or Kernigen & Ritchie for C. This is so obviously written by a practitioner - someone who has done it and has the scars to show it. Even the title tells you this is for the grown-ups - forget R and all that crap, all roads lead to SKLearn and TensorFlow, via anaconda a Jupyter. Buy this book and "Elements of Statistical Learning" and you have all the library you ever need. If you don't want to get bogged down in the maths, then just buy this one.

This is a really excellent book for somene looking into starting seriously with Tensorflow. It provides all these small bits and pieces missing from the (sparse) official documentation and it can save you from hours of search through stackoverflow and github repos. As an added benefit you are taught in a structured way how to start with things. A great introduction for most people, more experienced users should probably have a look first. Highly recommended!

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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

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.

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.