In this chapter, we learned about the main branches of machine learning techniques: supervised and unsupervised learning. We saw how to estimate a numeric or categoric attribute using supervised learning techniques such as KNN, decision tree, linear regression, and neural networks. We saw that it is possible to increase performance using ensembles that are techniques combining different supervised learning algorithms. We learned how to identify homogeneous groups using clustering techniques such as k-means and hierarchic clustering. We have also understood the importance of dimensionality reduction techniques such as the PCA to transform the features defining a smaller set of variables.

The next chapter shows an example of a business problem that can be faced using machine learning techniques. We will also see examples of both supervised and unsupervised learning techniques.