One of the most common ways to impart artificial intelligence into a machine is through machine learning. The world of machine learning is broadly divided into supervised and unsupervised learning. There are other divisions too, but we'll discuss those later.

Supervised learning refers to the process of building a machine learning model that is based on labeled training data. For example, let's say that we want to build a system to automatically predict the income of a person, based on various parameters such as age, education, location, and so on. To do this, we need to create a database of people with all the necessary details and label it. By doing this, we are telling our algorithm what parameters correspond to what income. Based on this mapping, the algorithm will learn how to calculate the income of a person using the parameters provided to it.

Unsupervised learning refers to the process of building a machine learning model without relying on...