Machine learning focuses on using data-driven approaches to create autonomous systems that can help us to make decisions with or without human supervision. In order to create these autonomous systems, machine learning uses a group of algorithms and methodologies to discover and formulate repeatable patterns in data. One of the most popular and powerful methodologies used in machine learning is the supervised machine learning approach. In supervised machine learning, an algorithm is given a set of inputs, called features, and their corresponding outputs, called target variables. Using a given dataset, a supervised machine learning algorithm is used to train a model that captures the complex relationship between the features and target variables represented by a mathematical formula. This trained model is the basic vehicle that is used for predictions. 

Predictions are made by generating the target variable of an unfamiliar set of features through the trained model.

Let's consider an example where we want to use supervised machine learning techniques to train a model that can categorize a set of emails into legitimate ones (called legit) and unwanted ones (called spam). First of all, in order to get started, we need examples from the past so that the machine can learn what sort of content of emails should be classified as spam. This content-based learning task for text data is a complex process and is achieved through one of the supervised machine learning algorithms. Some examples of supervised machine learning algorithms that can be used to train the model in this example include decision trees and naive Bayes classifiers, which we will discuss later in this chapter.