Introducing ML

ML is a set of techniques that enable computers to learn from patterns and behavior in data. It is often said that there are three different kinds of ML: Supervised, Unsupervised, and Reinforcement learning.

In supervised ML, an answer – called a label – is provided with the data to allow for an ML model to learn the patterns that will allow it to predict the correct answer. To put it simply, you give the model data and an answer, and it figures out how to predict correctly.

In unsupervised ML, no answer is provided to the model. The goal is usually to find clusters of similar pieces of data. For instance, you could use clustering to identify the different types of news articles present in a dataset of news articles, or to find topics that exist in a corpus of text. This is similar to what we have done with community detection.

In reinforcement learning, a model is given a goal and it gradually learns how to get to this goal. In many reinforcement...