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 machine learning from linear algebra 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)

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