01.01-introduction
01.02-what you should know
02.01-defining linear algebra
02.02-applications of linear algebra in ml
03.01-introduction to vectors
03.02-vector arithmetic
03.03-coordinate system
04.01-dot product of vectors
04.02-scalar and vector projection
04.03-changing basis of vectors
04.04-basis linear independence and span
05.01-matrices introduction
05.02-types of matrices
05.03-types of matrix transformation
05.04-composition or combination of matrix transformations
06.01-solving linear equations using gaussian elimination
06.02-gaussian elimination and finding the inverse matrix
06.03-inverse and determinant
07.01-matrices changing basis
07.02-transforming to the new basis
07.03-orthogonal matrix
07.04-gram-schmidt process
08.01-introduction to eigenvalues and eigenvectors
08.02-calculating eigenvalues and eigenvectors
08.03-changing to the eigenbasis
08.04-google pagerank algorithm
09.01-next steps
