In this chapter, we explored unsupervised learning methods that allow us to extract valuable signal from our data, without relying on the help of outcome information provided by labels.

We saw how we can use linear dimensionality reduction methods, such as PCA and ICA, to extract uncorrelated or independent components from the data that can serve as risk factors or portfolio weights. We also covered advanced non-linear manifold learning techniques that produce state-of-the-art visualizations of complex alternative datasets.

In the second part, we covered several clustering methods that produce data-driven groupings under various assumptions. These groupings can be useful, for example, to construct portfolios that apply risk-parity principles to assets that have been clustered hierarchically.

In the next three chapters, we will learn about various ML techniques for a key source of alternative data, namely, natural language processing for text documents.