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This is one of a handful of books on ML and R, and it’s by far the simplest in its coverage of content. For instance, it’s the only one I’ve looked at so far that has explained the difference between the Naive Bayes and Exact Bayes algorithms, although I haven’t looked at the Manning books on ML (e.g., Practical Data Science with R) some of which use R and some Python (e.g., Real-World Machine Learning). By a kindergarten approach to ML, I don’t mean to be disrespectful or to minimize the contribution of the book—presenting a topic at a simple basic level is worthwhile. The reasons I downgraded the book slightly were the large number of errors in the book (which look like material from previous editions that wasn’t caught) and some carelessness in what is presented. For some reason, in the chapter entitled “Overview of the Data Mining Process,” it bothered me a lot that the authors detail creation of dummy variables but don’t present factor variables until halfway through the book. Perhaps that was just the desire to present things at a basic level, however. I did learn some R functions I wasn’t familiar with from this book, and it does provide clear, explicit explanations of ML (or data mining) techniques. (For the life of me, I still don’t have a clear understanding of the differences between ML, data science, data analytics, and data mining—I suspect it is how the techniques and data are used that determines the difference, because the same techniques are used in all of these disciplines.) Hope this review was useful.