Suppose you want to predict whether tomorrow will be a sunny or rainy day. You can develop an algorithm that is based on the current weather and your meteorological knowledge using a rather complicated set of rules to return the desired prediction. Now suppose that you have a record of the day-by-day weather conditions for the last five years, and you find that every time you had two sunny days in a row, the following day also happened to be a sunny one. Your algorithm could generalize this and predict that tomorrow will be a sunny day since the sun reigned today and yesterday. This algorithm is a pretty simple example of learning from experience. This is what Machine Learning is all about: algorithms that learn from the available data.

In this book, you will learn several methods for building Machine Learning applications that solve different real-world tasks, from document classification to image recognition.

We will use Python, a simple, popular, and widely used programming language, and scikit-learn, an open source Machine Learning library.

In each chapter, we will present a different Machine Learning setting and a couple of well-studied methods as well as show step-by-step examples that use Python and scikit-learn to solve concrete tasks. We will also show you tips and tricks to improve algorithm performance, both from the accuracy and computational cost point of views.