I’ve long been fascinated by Artificial Intelligence and wanted to get started without knowing where to begin. This is why I picked up this book, thinking this would be a good starting point.
Truth be told, this was a good book and gave some insight, but not what I was currently looking for though. So for beginners into AI this is not the starting point.
What this book did give me though, was a brush-up on statistics, predictions and an introduction to R. Going through the book the author starts building up knowledge on how to use predictions, estimates, clustering and similar techniques in order to make a machine learn to know what to do next based on previous events. The theory is then backed up by practical using the language R.
The one chapter I liked the most was about building a simple recommendation engine on who to follow on Twitter based on your current profile. That sample got through some graph theory combined with clustering models, all summed up with some graphical elements summing up the points going through the chapter.
Unfortunately in the end I still felt left in the dark not knowing where to go from here. R seems like a really strong language for performing many types of statistical analysis, but I have yet to see how I should use that in some mainstream application. This is probably due to lack of knowledge on my side, but it just underlines my point about this not being a “beginners” book regarding machine learning.
To summarize it, the author did present basic statistical models that can be used in order to aid machine learning, all this combined with practical examples. But you need to have a higher baseline and previous knowledge about machine learning and ideas about in to utilize it in order to fully enjoy this book.
Buy this book at O'Reilly and support my blog
Machine Learning for Hackers