Linear Algebra, Data Science, Applied Machine Learning.
And there you have it. The components obtained from SVD are actually singular vectors, which are really just the eigenvectors of the associated symmetrical square matrix obtained from multiplying A by its own transpose. From the last image above, we can see that taking the last row of VT gives us