Machine learning (ML) teaches machines how to carry out tasks by themselves. It is that simple. The complexity comes with the details, and that is most likely the reason you are reading this book.
Maybe you have too much data and too little insight, and you hoped that using machine learning algorithms will help you solve this challenge. So you started to dig into random algorithms. But after some time you were puzzled: which of the myriad of algorithms should you actually choose?
Or maybe you are broadly interested in machine learning and have been reading a few blogs and articles about it for some time. Everything seemed to be magic and cool, so you started your exploration and fed some toy data into a decision tree or a support vector machine. But after you successfully applied it to some other data, you wondered, was the whole setting right? Did you get the optimal results? And how do you know there are no better algorithms? Or whether your data was "the right one"?
Welcome to the club! We, the authors, were at those stages once upon a time, looking for information that tells the real story behind the theoretical textbooks on machine learning. It turned out that much of that information was "black art", not usually taught in standard textbooks. So, in a sense, we wrote this book to our younger selves; a book that not only gives a quick introduction to machine learning, but also teaches you lessons that we have learned along the way. We hope that it will also give you, the reader, a smoother entry into one of the most exciting fields in Computer Science.