Supervised and Unsupervised Learning

ML is divided into two main categories: supervised and unsupervised learning.

Supervised Learning

Supervised learning consists of understanding the relationship between a given set of features and a target value, also known as a label or class. For instance, it can be used for modeling the relationship between a person's demographic information and their ability to pay loans, as shown in the following table:

Figure 1.18: The relationship between a person's demographic information and the ability to pay loans

Models trained to foresee these relationships can then be applied to predict labels for new data. As we can see from the preceding example, a bank that builds such a model can then input data from loan applicants to determine if they are likely to pay back the loan.

These models can be further divided into classification and regression tasks, which are explained as follows.

Classification tasks...