Machine learning teaches machines to learn 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. Maybe you hope that, by using machine learning algorithms, you can solve this challenge, so you started digging into the algorithms. But perhaps after a while you became puzzled: which of the myriad of algorithms should you actually choose?
Alternatively, maybe you are simply more generally interested in machine learning and you have been reading blogs and articles about it for some time. Everything seemed to be magic and cool, so you started your exploration and fed some data into a decision tree or a support vector machine. However, after you successfully applied these to some other data, perhaps you wondered: was the whole setting right? Did you get optimal results? How do you know that there are no better algorithms? Or whether your data was the right kind?
Welcome to the club! All of us authors were once at those stages, looking for information that tells the stories behind the theoretical textbooks about machine learning. It turned out that much of that information was black art, not usually taught in standard text books. 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 the lessons we learned during our careers in the field. We hope that it will also give you a smoother entry into one of the most exciting fields in computer science.
