Machine learning is a process through which we use data to train models. These models are then used to make predictions on a new set of data that the model hasn't seen before. There are different types of machine learning models, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
The data in a supervised learning model should have labels or an end result. Supervised learning models are broadly classified into classification and regression learning models. In an unsupervised learning process, we may not know the labels or the outcomes beforehand. Clustering is an example of unsupervised learning. Semi-supervised learning models use a combination of supervised and unsupervised learning. In reinforcement learning, an agent learns to navigate an environment with feedback mechanisms that reinforce the goal maximizing actions.
In this chapter, the machine learning process will be demonstrated through examples. The types of machine learning models will be explained and the different evaluation metrics are discussed. We will learn to perform exploratory data analysis and implement a simple linear model in R.