Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
My rating: 5 of 5 stars

"Machine Learning is the science (and art) of programming computers so they can learn from data."

It is December 27th, four days until the end of the year, and I am four books short of my Goodreads 2020 Reading Challenge goal of 60 books. Never abandon hope! I will review two computer science books that were tremendously important to me and my students in 2020, books that helped me return to the field of neural networks and machine learning in general and helped my outstanding research student complete her challenging and advanced research project with extraordinary success.

I worked with neural networks (NN) in the late 1980s and early 1990s and even co-taught a psychology/computer science course on neural network learning. However, in the 1990s it had become clear that the limits of what the then traditional NN architecture can achieve had been reached and the scientific community basically abandoned NNs as the preferred approach to machine learning. Yet beginning in the first decade of the 21st century we witnessed the rebirth of the NN idea, primarily via various multi-level NN models, such as convolutional neural networks (CNNs) developed by Le Cun, Hinton, and others. Currently, CNNs achieve truly spectacular (without exaggeration one can say 'superhuman') results in various areas of artificial intelligence (AI) and machine learning (ML).

The recent explosion of research and commercial interest in ML resulted in an avalanche of books published on the topic, particularly "popular" books (ones that can serve as tutorials of sorts), addressed to computer science practitioners of various level of preparation, from complete novices to advanced. The range of quality of the books is even more vast. I worked with, read, or at least scanned thoroughly over 20 ML books and to me Hands-On Machine Learning is by far the best text, one that can serve for a wide variety of purposes: on one hand, it can serve as an ML textbook, on the other it can be used as a tutorial for particular methods of ML. (I will review the other great ML book, one that focuses purely on NN, the day after tomorrow. By the way, I was amazed how many bad, totally useless ML books have been published. Christmas spirit prevents me from listing their titles.)

Aurélien Géron, the author of Hands-On Machine Learning, comes with impressive industry credentials. He served as the Product Manager of YouTube video classification at Google, and held several senior positions in artificial intelligence engineering in various companies.

The first two chapters of the book, which belong to the first part entitledThe Fundamentals of Machine Learning, are an absolute must read for anyone interested in studying ML. The author presents the 'landscape of machine learning' and shows a typical ML project 'end-to-end', including data preparation and preprocessing as well as selecting, training, and fine-tuning the model.

The next six chapters of Part I focus on specific ML approaches and their mathematical background. We read about the methods of classification, the Support Vector Machines approach, including the 'kernel trick,' decision trees, ensemble learning and random forests. I love the solid yet very accessible presentation of the math background in the chapter on gradient descent, various types of regression, and regularization. Part I closes with a nice chapter about dimensionality reduction, which focuses on the method of Principal Component Analysis.

Part II of the book, titled Neural Networks and Deep Learning, gives a great overview of the so-called 'deep learning' approach: the reader will learn about the 'classical' NN approach, and then will be gradually introduced to the multi-level NN architecture, CNNs, recurrent NNs, and autoencoders.

The author's reliance on the production-ready Scikit-Learn and TensorFlow Python frameworks rather than on developing own toy versions of various algorithms is commendable. Scikit-Learn, a free software library of machine learning tools, is one of the best things developed in computer science in the last 50 years. It is a splendid manifestation of the power of open-source software.

From a teacher's point of view, the book is excellent! I believe Hands-On Machine Learning is great for the students too. It comes with a lot of interesting Python code samples, in the form of Jupyter notebooks. And the code works! The students can learn a lot by rewriting and extending the sample code.

Very, very highly recommended book! And I am going to round up my extremely high rating of

Four-and-a-half stars.

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