I bought this book a couple of years ago as machine learning and deep neural networks were becoming the big news in the smart algorithms world, and while it was a bit old even then, the concepts have aged well. I had hoped for a bit more answering of why the more complicated algorithms can be expected to work, but this book was not written for that audience. Instead, it explains when the algorithms can be used, and how to implement and use them appropriately. This is an important thing to know, as well, and the book is much more on the applied than theoretical side.
The biggest problem with the book are the incorrect lines of code that pop up every once and a while. This is annoying, and at least one of the errors was incredibly subtle (importing pylab overwrote some of the random functions from the library random). The errata onlin has some helpful answers, but also is full of things that are not.
Given the book's age, it also is written with python2. Python3 is much preferred now, and pretty much all of the libraries used in the book are available for python3 (though they sometimes change names). This requires minor translation efforts. The major problem is that many of the API relying on websites on the internet no longer exist. This means some of the exercises are not possible, or you will have to use some other API. Pandas can make up for some of these deficiencies.
Overall, I found the book enlightening and am glad it forced me to actually write out and get the code working for myself. Experience is a great teacher and if you go through what's given in the book, you'll solidly understand the basics as well as be able to use many algorithms (genetic algorithms, naive Bayesian classifiers, recommendation methods, optimization, some database ideas) that should be useful in commonly faced problems. I did not go through many of the exercises at the end of the chapters, but most of them are straightforward applications are extensions of the ideas in the book, and seem like they would provide a decent challenge.
If you'd like to understand the basics of the above ideas, and have code to use them on data with, then this is a fairly good choice. I would recommend looking around to see if people recommend a newer edition or book with similar ideas, however, since it is a book written over a decade ago. On the other hand, the explanations and figures are helpful and do not over-complicate things.
ISBN-13: 978-0596529321
ISBN-10: 0596529325
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Top reviews from other countries
R. Whitehead
Fascinating and inspiring
Reviewed in the United Kingdom on March 13, 2011Joshua Newnham
Great introduction to machine learning
Reviewed in the United Kingdom on December 17, 2013
