Welcome to the "Linear Algebra in ML and Data Science" repository! This repository contains Jupyter Notebook files showcasing the practical implementation of linear algebra concepts in the fields of machine learning and data science. The notebooks cover various topics such as solving systems of equations, determinants, inverses, transposes, transformations, span, bases, eigenvalues, and eigenvectors.
Linear algebra plays a fundamental role in understanding and applying concepts in machine learning and data science. This repository aims to provide practical examples and explanations of linear algebra concepts, enabling you to develop a strong foundation in this subject.
The repository contains the following Jupyter Notebook files:
- 01-System-of-Equations
- 02-Singular-Non-Singular-Matrices
- 03-Determinants
- 04-Inverse-Matrices
- 05-Matrix-Transpose
- 06-Linear-Transformations
- 07-Span-and-Basis
- 08-Eigenvalues-Eigenvectors
Feel free to explore each notebook to gain a practical understanding of the respective topic.
Contributions to this repository are welcome! If you have any improvements, bug fixes, or additional notebook files related to linear algebra in ML and data science, please feel free to submit a pull request. Make sure to follow the repository's code of conduct and guidelines for contributing. eusing the code or content for your own projects.