The amount of data collected for various purposes in society has increased enormously in the last few decades. Machine learning is a way of making sense of all this data by leveraging what we know about the data. In the generalized picture of machine learning, the computer first learns from a given dataset (training) and creates a generalized model to represent it. With this model, it is possible to predict various outcomes, results, and groupings (classes). In this chapter, we will cover the following topics:
Linear regression with machine learning algorithms
Clustering with machine learning algorithms
Feature selection—a preprocessing method to select what is most important
Classification with different machine learning algorithms and kernels
Before getting started, I will give you a brief introduction to machine learning and the package that we will use: Scikit-learn.