Supervised learning is the simplest way of teaching a model about how the world looks. Showing how a given combination of input variables leads to a certain output, that is, using labeled data, makes it possible for a computer to predict the output for another similar dataset that it has never seen. Unsupervised learning deals with finding patterns and useful insights into non-labeled data.
We will study different types of machine learning models, trying to understand the details and actually performing the necessary calculations so that the inner workings of these models are clear and reproducible.
In this chapter, the following topics will be covered:
- Understanding supervised learning with multiple linear regression
- Understanding supervised learning with decision trees
- Understanding unsupervised learning with clustering
