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: https://www.youtube.com/user/EvolutionOfScience/playlists

Part 2(Advanced): Arthur Parzygnat

YT : https://www.youtube.com/channel/UCig5aK06RoHZomGrjhS_6gg

License: Creative Commons Attribution license (reuse allowed)

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