I think it would be great to add videos on singular value decomposition and principal component analysis towards the end of the linear algebra track, after the videos on eigenvectors.  SVD and PCA seem like extremely important concepts for machine learning and they would show a real-world use of eigenvalues and eigenvectors.  There's not a whole lot of content about them online, and much of the existing content either lacks examples of the calculations, is too abstract, or is poorly explained (in my opinion)