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-

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

So, the **Topics to Learn in Linear Algebra are-**

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

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

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

Eigenvalues, eigenvectors, diagonalization, and singular value decomposition.

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.

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

The next question we have-

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**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-

**Click to continue reading...**

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