1. Introduction to Clustering

Overview

Finding insights and value in data is the ambitious promise that has been seen in the rise of machine learning. Within machine learning, there are predictive approaches to understanding dense information in deeper ways, as well as approaches to predicting outcomes based on changing inputs. In this chapter, we will learn what supervised learning and unsupervised learning are, and how they are applied to different use cases. Once you have a deeper understanding of where unsupervised learning is useful, we will walk through some foundational techniques that provide value quickly.

By the end of this chapter, you will be able to implement k-means clustering algorithms using built-in Python packages and calculate the silhouette score.