Summary

In this chapter we introduced the basic machine-learning concepts and terminology that will be used in the rest of the book. Tutorials of the most relevant libraries (NumPy, pandas, and matplotlib) used by machine-learning professionals to prepare, t manipulate, and visualize data have been also presented. A general introduction of all the other Python libraries that will be used in the following chapters has been also provided.

You should have a general knowledge of what the machine-learning field can practically do, and you should now be familiar with the methods employed to transform the data into a usable format, so that a machine-learning algorithm can be applied. In the next chapter we will explain the main unsupervised learning algorithms and how to implement them using the sklearn library.