Here, we’ll cover the linear algebra concepts behind the machine learning algorithms for dimensionality reduction. We'll learn about vectors and matrices, linear equations, eigenvalues and eigenvectors, and more.

This section explains the intricacies of the dimensionality reduction process and clarifies why this technique is essential when working with large datasets.

In this part of the course, we explore the Principal Component Analysis (PCA) - one of the most widely used algorithms for dimensionality reduction. We'll demonstrate a practical example combining both feature extraction and feature selection techniques to achieve the desired goal - reducing the number of dimensions in our dataset.

In this section, we'll cover another dimensionality reduction technique called Linear Discriminant Analysis (LDA). Here, we'll go through another practical example, showing the methodology behind LDA and its efficiency. We'll also make a comparison between this algorithm and PCA, introducing the advantages of both approaches.