https://github.com/zeeshier/linear-algebra
Linear Algebra for Machine Learning & Data Science
https://github.com/zeeshier/linear-algebra
data-science linear-algebra machine-learning math mathematics
Last synced: about 1 year ago
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Linear Algebra for Machine Learning & Data Science
- Host: GitHub
- URL: https://github.com/zeeshier/linear-algebra
- Owner: Zeeshier
- Created: 2024-12-08T17:51:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-17T11:43:02.000Z (over 1 year ago)
- Last Synced: 2025-02-17T12:32:56.379Z (over 1 year ago)
- Topics: data-science, linear-algebra, machine-learning, math, mathematics
- Language: Jupyter Notebook
- Homepage:
- Size: 1.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Linear Algebra for Machine Learning and Data Science**
Welcome to the **Linear Algebra** repository! This collection of Jupyter Notebooks is designed to provide a comprehensive understanding of various linear algebra concepts and techniques, all implemented in Python. Whether you're a beginner or looking to deepen your knowledge, this repository serves as a hands-on guide.
## **Table of Contents**
1. [Systems of Equations](01.%20Systems%20of%20Equations)
2. [Row Echelon Form and Rank](02.%20Row%20Echelon%20Form%20and%20Rank)
3. [Gaussian Elimination](03.%20Guassian%20Elimination)
4. [Vector Operations](04.%20Vector%20Operations)
5. [Linear Transformations](05.%20Linear%20Transformations)
6. [Eigenvalues and Eigenvectors](06.%20Eigenvalues%20and%20Eigenvectors)
---
## **Description**
This repository contains a series of Jupyter Notebooks, each focusing on a specific topic within linear algebra. The topics covered include:
- **Systems of Equations**: Methods to solve linear systems.
- **Row Echelon Form and Rank**: Techniques to simplify matrices and determine their rank.
- **Gaussian Elimination**: Step-by-step process to solve systems of linear equations.
- **Vector Operations**: Fundamental operations involving vectors.
- **Linear Transformations**: Understanding mappings between vector spaces.
- **Eigenvalues and Eigenvectors**: Concepts and computations related to eigenvalues and eigenvectors.
---
## **Getting Started**
To explore the notebooks:
1. **Clone the repository**:
```bash
git clone https://github.com/Zeeshier/Linear-Algebra.git
cd Linear-Algebra