7 of 8 people found the following review helpful
Data science not machine learning,
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This review is from: Machine Learning for Hackers (Paperback)
This book isn't really about machine learning. There is relatively little in the book about the machine learning algorithms themselves. Most of it is plumbing: how to munge the data in using R. And there are some nice motivating examples on using the ggplot2 library in R to visualise the results. (However, on the downside, plots which require colour to be understood are presented in black and white, which doesn't help! The R code mostly works, so usually it is possible to produce your own, but it seems a waste of paper to output plots that show male vs female dots, say, in different shades of grey on the same graph.)
Also, when the algorithms are presented there are sometimes some serious errors (see the review on amazon's US site "Erroneous but entertaining" for more details). The single most shocking example was when a series of numbers was said to show the percentages of variation explained by an analysis, but the series added up to much than 100%. This was by no means the only error, however. The cumulative effect for me was that as I got further and further through the book, I began to have less and less trust in what was being presented to me.
I would characterise the book as being for hackers in the sense that you are encouraged to try a technique and see if it works. One good point is that the book emphasises having a separate test set from your training set.
Trying techniques until you find one that works is probably a good place to start, especially if your interest is in starting to learn the broader field of data science -- getting the data in, analysing it, visualising it -- rather than specialising in the selection and choice of machine learning algorithms themselves (for which Andrew Ng's coursera online course is a far better choice).