This repository is dedicated to exploring various concepts of Linear Algebra using Numpy in Python, illustrated through Jupyter Notebooks.

1. 01. Introduction.ipynb: Introduction to basic linear algebra concepts.
2. 02. Index, Slice, Reshape Numpy array.ipynb: Demonstrates array manipulation techniques in Numpy.
3. 03. Broadcasting in Numpy.ipynb: Explains broadcasting in Numpy for array operations.
4. 04. Vectors in Numpy.ipynb: Covers vector operations in Numpy.
5. 05. Vector Norm.ipynb: Discusses the concept and calculation of vector norms.
6. 06. Matrices and Matrix Arithmetic.ipynb: Basics of matrix operations in Numpy.
7. 07. Types of Matrices.ipynb: Differentiates various types of matrices.
8. 08. Matrix Operation.ipynb: Advanced matrix operations and techniques.
9. 09. Sparse Matrices.ipynb: Handling sparse matrices in Numpy.
10. 10. Tensors and Tensor Arithmetic.ipynb: Introduction to tensors and their operations.
11. 11. Matrix Decomposition.ipynb: Methods of matrix decomposition.
12. 12. Eigen Decomposition.ipynb: Exploring eigenvalues and eigenvectors.
13. 13. Singular Value Decomposition.ipynb: Implementation of singular value decomposition.
14. 14. PseudoInverse.ipynb: Understanding and calculating the pseudoinverse of matrices.
15. 15. Dimensionality Reduction using SVD.ipynb: Using singular value decomposition for reducing dimensions.
16. 16. Multivariate Statistics.ipynb: Principles of multivariate statistics.
17. 17. Covariance.ipynb: Techniques for calculating covariance.

This project is licensed under the MIT License.

Thanks to all contributors and the mathematics community.