Master Linear Algebra for Machine Learning 📊

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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