How to Learn Linear Algebra for Machine Learning in 2021

Most of us have question- Why Linear Algebra for Machine Learning?

Right…?

So the answer is- because Linear algebra is used in data preprocessing, data transformation, dimensionality reduction, and model evaluation.

The next question we have-

What Linear Algebra Topics are required for Machine Learning & Data Science?

So, the Topics to Learn in Linear Algebra are-

1. Basic properties of a matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, and determinant.

2. Inner and outer products, matrix multiplication rule and various algorithms, and matrix inverse.

3. Matrix factorization concept/LU decomposition, Gaussian/Gauss-Jordan elimination, solving Ax=b linear system of an equation.

4. Eigenvalues, eigenvectors, diagonalization, and singular value decomposition.

5. Special matrices: square matrix, identity matrix, triangular matrix, the idea about sparse and dense matrix, unit vectors, symmetric matrix, Hermitian, skew-Hermitian and unitary matrices.

6. Vector space, basis, span, orthogonality, orthonormality, and linear least square.

The next question we have-

What are the Resources to learn Linear Algebra?

So I have chosen 8 Best Linear Algebra Courses for Machine Learning & Data Science and here is the list-
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