Hi,
Thank you for the crisp explanation of the linear algebra concepts…Helps me take a fresh look at the ML algorithms which makes so much more sense now with ease.
Other than eigenvalue decomposition, in data science we have used Singular Value Decomposition of matrices also. Can you please touch upon that topic also and why/when its necessary in ML.
Dear Soumyabrata_Das,
Thank you for your question! One example in ML would be PCA which is essentially trying to find the orthonormal basis of vectors for a matrix (e.g., your input data). These vectors are in fact the eigenvectors of the covariance matrix which are ranked by their eigenvalues.
If you need more details please refer to THIS LINK 
All the best,
Kiavash