Add comprehensive linear algebra topics to module

Added extensive coverage of advanced linear algebra topics:

1. Solving Systems of Linear Equations
   - Gaussian elimination and row reduction
   - Solving Ax = 0 (homogeneous systems)
   - Solving Ax = b (non-homogeneous systems)
   - Pivot variables and free variables

2. Column Space and Nullspace
   - Column space (range) computation
   - Nullspace (kernel) computation
   - Rank-nullity theorem

3. The Four Fundamental Subspaces
   - Column space, row space, nullspace, left nullspace
   - Orthogonality relationships
   - Complete subspace analysis

4. Determinants
   - Properties and computation
   - Cofactor expansion
   - Cramer's rule for solving systems

5. Diagonalization
   - Conditions for diagonalizability
   - Computing powers of matrices
   - Applications in ML

6. Matrix Exponentials
   - Definition and computation
   - Solving differential equations
   - Applications in continuous-time systems

7. Pseudoinverse (Moore-Penrose)
   - SVD-based computation
   - Left and right inverses
   - Applications in least squares

8. Positive Definite Matrices
   - Properties and checking
   - Cholesky decomposition
   - Applications in ML (covariance, kernels)

9. Complex Matrices and FFT
   - Hermitian and unitary matrices
   - Fast Fourier Transform
   - Applications in signal processing and CNNs

All topics include ML-focused explanations, Python code examples, and practical applications. Updated table of contents with all new sections.

Author: NabidAlam