In this chapter, you will learn how to apply unsupervised learning techniques to identify patterns and structure within datasets.
Unsupervised learning techniques are a valuable set of tools for exploratory analysis. They bring out patterns and structure within datasets, which yield information that may be informative in itself or serve as a guide to further analysis. It's critical to have a solid set of unsupervised learning tools that you can apply to help break up unfamiliar or complex datasets into actionable information.
We'll begin by reviewing Principal Component Analysis (PCA), a fundamental data manipulation technique with a range of dimensionality reduction applications. Next, we will discuss k-means clustering, a widely-used and approachable unsupervised learning technique. Then, we will discuss Kohenen's Self-Organizing Map (SOM), a method of topological clustering that enables the projection of complex datasets...