https://github.com/sadegh15khedry/ml-algorithms-from-scratch

This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.
https://github.com/sadegh15khedry/ml-algorithms-from-scratch

cnn dbscan decision-trees deep-learning gan ica jupyter-notebook knn-classification linear-regression logestic-regression lstm machine-learning naive-bayes-classifier python random-forest rnn svm t-sne transformer

Last synced: 3 days ago
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This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

• Host: GitHub
• URL: https://github.com/sadegh15khedry/ml-algorithms-from-scratch
• Owner: sadegh15khedry
• License: mit
• Created: 2024-07-02T15:46:24.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-08-17T10:21:07.000Z (3 months ago)
• Last Synced: 2024-08-17T11:32:36.198Z (3 months ago)
• Topics: cnn, dbscan, decision-trees, deep-learning, gan, ica, jupyter-notebook, knn-classification, linear-regression, logestic-regression, lstm, machine-learning, naive-bayes-classifier, python, random-forest, rnn, svm, t-sne, transformer
• Language: Jupyter Notebook
• Homepage:
• Size: 4.48 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE
  • Support: Support-Vector_Machines/LICENSE

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README

        
# ML Algorithms From Scratch

Welcome to the **ML-Algorithms-from-scratch** repository! This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

## Table of Contents

1. [Introduction](#introduction)

2. [Algorithms Implemented](#algorithms-implemented)

3. [Installation](#installation)

4. [Usage](#usage)

5. [Project Structure](#project-structure)

6. [Contributing](#contributing)

7. [License](#license)

## Introduction

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Understanding the inner workings of these algorithms is crucial for becoming proficient in machine learning. This repository contains implementations of various machine learning algorithms from scratch using Python.

## Algorithms Implemented

- [x] Linear Regression

- [x] Logistic Regression 

- [x] K-Nearest Neighbors (KNN)

- [x] Naive Bayes Classifier

- [x] Decision Tree

- [x] Random Forest

- [x] Support Vector Machine (SVM)

- [x] K-Means Clustering 

- [x] Principal Component Analysis (PCA) 

- [ ] Neural Networks  (will be implemented)

- [ ] (More algorithms will be added...)

## Installation

Use the Installation guide provided in the each project. 

ML-Algorithms-From-Scratch

Use each project using the installation guide provided in the project's folder.

## Usage

Each algorithm has its own dedicated folder containing the implementation and a Jupyter notebook with examples of how to use it. To run the notebooks, you need to have Jupyter installed:

pip install jupyter

jupyter notebook

Open the notebook you are interested in and run the cells to see the algorithm in action.

## Project Structure

## Contributing

Contributions are welcome! If you have any suggestions, improvements, or new algorithms to add, please feel free to create a pull request or open an issue.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.