Linear Algebra for Machine Learning
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Section 1: Introduction
- Why Learn Linear Algebra?
- Download Resources
- Types of Tensors
- Scalars
- Vectors
- L1 and L2 Norm
- Angle between Vectors
- Projections and Unit Vector
- Basis, Orthogoanal and Orthonormal Vectors
Section 2: Matrices
- Types of Matrices
- Matrix Operations
- Orthogonal and Orthonormal Matrices
Section 3: Essential Equations
- Equation of line, plane and hyperplane
- Geometric interpretation of Weights vector
- Equation of Circle, Sphere and Hyperplane
Section 4: Towards SVD and PCA
- Eigenvalues and Eigenvectors
- Eigen Decomposition
- Singular Value Decomposition (SVD)
- Principal Component Analysis (PCA)