Chapter 3 moves to Naive Bayes methods - classical statistics. This is reasonably well described but leaves out so much. There is code to help you try things out, but without a deeper understanding of the ideas it is difficult to see what use it all is.  Next we have decision trees and then on to one of of the most difficult topics in AI - hidden Markov chains. This is about as mathematically complex as you can get and, as you can imagine, the coverage is very slight. We do get a rough explanation of the Virterbi algorithm and an example involving parts-of-speech tagging.

Chapter 7 introduces the Support Vector Machine with a reasonable explanation of the idea of linear separability and how kernels can be used to convert the decision boundary to a curve, but it really doesn't explain how or why the SVM is any better than any other linear discriminant function; and of course the classical linear discriminant isn't discussed at all.

Finally, for many readers, we get to neural networks. This is a reasonably good account of the ideas, but not really incorporating any of the new ideas that have produced the neural network revolution. You don't get any discussion of drop out or batch training, but it is still an adequate intro if you really know nothing about the topic. I found myself disagreeing with many of the suggestions on how to build a network and was puzzled by the inclusion of sine in the list of possible activation functions as it is very far from being a common choice. The suggestion that one hidden layer is enough was also something of an oversimplification in this era of "deep" neural networks with many layers. Yes they are harder to train and they do need more data, but these are the networks that are making headlines. The networks described here are more like small networks standing in for more traditional statistical methods. You don't find any discussion of convolutional, adversarial or feedback networks, and this isn't unreasonable given the size of the book and its level - but again these are the things that the new AI revolution is based on.

Chapter 9 is a fairly conventional account of clustering, which is more of a classical statistical method than machine learning. The penultimate chapter is about improving the data we feed to models and the final chapter is an overview.

This book really doesn't stand a fair chance. Machine learning is a topic that doesn't make much sense without at least some math. The math isn't rocket science, but without it you are really working with things you don't really understand. Although each chapter starts out by being conversational and telling you a story without math, sooner or later it simply presents a few equations to clarify but with no attempt to explain what they mean.

There are some very nice uses of geometry to explain the way the data is being treated, but this approach isn't used enough. Most machine learning can be understood better with some insights into what the data looks like in the data space.

If you aren't good at math, this book isn't going to help you understand machine learning or the math you need. At best it could be an easy read refresher for someone with most of the math, but not enough of the AI. You would still need to go away and read some more, but it is a reasonble overview as far as it goes. It does not, however, give you any insight into the current amazing state of machine learning that is responsible for so much interest.

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