A Primer (or Refresher) On Linear Algebra for Data Science

[belisards]

0:05 Introduction 0:35 What is linear algebra? 1:00 Applications of linear algebra 1:52 Linear algebra in data science 3:20 Vectors and matrices 4:17 Matrix-vector multiplication 5:25 Rotate an image using matrix multiplication 7:11 Matrix inverse 7:40 Geometry of least squares 11:42 Intermezzo: orthogonality 13:37 Matrix decomposition 14:15 Singular Value Decomposition (SVD) 15:24 SVD of a 4x3 matrix 18:14 Code: decomposing Big Ben 18:30 Approximations of an image with SVD 20:30 Only the largest singular values matter 21:51 Back to linear regression 23:01 Linear regression in sklearn uses SVD 24:10 Recapitulation 24:26 Extra resources 25:52 Why should I not inverse matrix numerically? 26:20 Inverting the Hilbert matrix 27:50 Q&A

https://www.youtube.com/watch?v=Qz58vTa8-SY