The lecture discusses the geometrical interpretation of eigenvalues and eigenvectors in Principal Component Analysis (PCA). It emphasizes data reduction techniques and the significance of understanding maximum directions in two-dimensional data. The importance of subscribing to the channel for further insights is also highlighted throughout the presentation.