Unsupervised Learning II

This second chapter on unsupervised learning will focus on dimensionality reduction. Dimensionality reduction is a crucial step in the preprocessing stage of machine learning. The idea behind dimensionality reduction is to simplify complex high-dimensional data into a lower-dimensional representation, while preserving the essential structure and relationships within the data. This can lead to improved visualization, faster training time, and increased accuracy in machine learning models.

There are several methods for dimensionality reduction, including principal component analysis (PCA), linear discriminant analysis (LDA), t-distributed stochastic neighbor embedding (t-SNE), and more. Each method has its own strengths and weaknesses, and the appropriate technique will depend on the characteristics of the data and the desired outcome.

In this chapter, we will explore the various techniques for dimensionality reduction and discuss when each method is best suited. We will also demonstrate how to implement these techniques using Python and scikit-learn.