In this course you will learn everything you need to know about linear algebra for #machine #learning. First part of this linear algebra course you will find the basics of #linear #algebra and second part of this course discussed about advanced linear algebra. This will allow to understand #machinelearning from #linearalgebra hence mathematical point of view. ** Topics Covered ** Vectors: Basic vectors notation, adding, scaling (0:00) Explaining the vector dot product (8:41) Introducing the vector cross product (15:58) More example of vector cross product (23:40) Thinking further about the cross product (30:15) Indroducing scaler triple product of vectors (38:10) Introduction to the matrix and matrix product (48:10) How to find determinant (58:00) Finding eigenvalues (1:8:0) Finding eigenvactors (1:17:00) Least square approximation: Introduction (1:36:00) Least square approximation: Fitting data to a straight curve(1:57:00) Least square approximation: the inverse of A transpose time A(2:38:11) Hamming Matrices (2:50:00) The functional calculus (3:27:00) Affine subspaces and transformations (4:15:00) Stochastic maps (05:02:00) ** Attribution ** Part 1(Basics): Simon Benjamin YT Channel: null Part 2(Advanced): Arthur Parzygnat YT : https://www.youtube.com/channel/UCig5aK06RoHZomGrjhS_6gg License: Creative Commons Attribution license (reuse allowed) ** Join our community ** Join our FB Group: https://www.facebook.com/groups/cslesson Like our FB Page: https://www.facebook.com/cslesson/ Website: https://cslesson.org/