Constructing basic machine learning algorithms

 As mentioned in the last chapter, machine learning is a term that was developed as a reaction to the first AI winter. Today, we generally consider machine learning to be the overarching subject area for deep learning and ANNs in general.

Most machine learning solutions can be broken down into either a classification problem or a regression problem. A classification problem is when the output variables are categorical, such as fraud or not fraud. A regression problem is when the output is continuous, such as dollars or site visits. Problems with numerical output can be categorical, but are typically transformed to have a categorical output such as first class and second class.

Within machine learning, we have supervised algorithms and unsupervised algorithms. In this section, we will introduce these types of algorithms and explore two examples of each.