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 Machine learning and Python blog Series.
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.