Chapter 4 is where the book starts on it core subject matter. This chapter deals with a simple clustering method - k-means. After showing how to write a program to compute the mean, the chapter moves on to the full k-means clustering program which is applied to some easy examples. The final part of the chapter deals with how to investigate the value of k to get a good estimate of the number of groups. There are some places where splitting the program over a page and wrapping of lines makes the code harder to read than it could be. 

Moving on from clustering, Chapter 5 looks at classification with the first method being the k-nearest neighbour classifier and the second being a Bayes classifier. At the end of the chapter the more complex SVM and Random Forest classifiers are introduced, but using code that you have to download. From this point on the book doesn't develop the code to do the core computations, it just demonstrates how to use pre-written code.

Chapter 6 is devoted to the association rule algorithm which attempts to find relationships between, say, things that a person has bought. Again we use pre-written code and simply import the libraries that do the work.

Chapter 7 is on prediction and mostly about regression, but some time series techniques are introduced towards the end. The regression is restricted to classical linear and polynomial. The time series is limited to filtering and seasonality with a mention of Fourier analysis. On the whole this chapter could have probably been left out - regression is hardly a machine learning technique and without covering some of the more advanced forms it is even less so.

The next three chapters are probably for most readers the star of the book as they are on neural networks using TensorFlow.js. TensorFlow is one of the standard ways of working with neural networks and TensorFlow.js lets you do machine learning in the browser. Chapter 8 introduces the basics of neural networks and shows how to use TensorFlow.js to solve the XOR problem. Chapter 9 goes deeper and explains convolutional neural nets and recurrent networks. It uses the classic MNIST handwriting data to train a convolutional network. Chapter 10 is a brief introduction to natural language processing. It covers many of the standard techniques such as stemming, text distance and so on. The chapter closes with a quick look at neural networks in language processing, but no programs are presented.

Chapter 11 is about real time machine learning and covers using streaming and data pipelines and cloud services. It is more a general awareness raising discussion than a practical guide. The final chapter is about choosing algorithms for your application.

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Hands-on Machine Learning with JavaScript is a book of two halves. The first half shows you how to write simple JavaScript programs to implement algorithms. The second half admits defeat and uses library code to implement the difficult parts. This isn't unreasonable as the idea of filling a book with, say, the code needed to compute a regression or, even more unthinkable, a convolutional neural network, is not a good one. In practice you are more likely to use off the shelf code that write things from scratch. So if you were expecting that the book would show you how to code these things are this level you will be disappointed.

The level of coverage is very shallow and very little math is used to make things exact. This is a good thing if all you want is a practical introduction to the basics of machine learning. Notice, however, that there are lots of omissions - only one clustering algorithm given, nothing on more advanced regression techniques, nothing on reinforcement learning and so on. Again, this isn't unreasonable given the size of the book and making a selection is a good idea. However, most of the exciting things going on in machine learning are about neural networks and reinforcement learning, and this isn't covered.

If you want a book that will get you started with some aspects of practical machine learning using JavaScript, then this is a good and easy-to-read choice.

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