https://github.com/ali-el-badry/machine-learning-algorithm

It is a Repo that contain different type of Machine Learning Algorithm like Regression ,classification and clustering that will be added soon
https://github.com/ali-el-badry/machine-learning-algorithm

ai data-science data-visualization decision-tree feature-selection knn linear-regression logestic-regression machine-learning modelling random-forest svc svm titanic-kaggle xgboost xgboost-classifier

Last synced: about 1 year ago
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It is a Repo that contain different type of Machine Learning Algorithm like Regression ,classification and clustering that will be added soon

• Host: GitHub
• URL: https://github.com/ali-el-badry/machine-learning-algorithm
• Owner: Ali-EL-Badry
• Created: 2024-09-08T14:08:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-12T16:15:45.000Z (about 1 year ago)
• Last Synced: 2025-02-12T17:26:55.444Z (about 1 year ago)
• Topics: ai, data-science, data-visualization, decision-tree, feature-selection, knn, linear-regression, logestic-regression, machine-learning, modelling, random-forest, svc, svm, titanic-kaggle, xgboost, xgboost-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 3.07 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📌 Machine Learning Repository

## Overview

This repository serves as a comprehensive guide to **Machine Learning**, covering all major types of ML techniques. It contains **Jupyter Notebook examples** for each type of machine learning, including **Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning**. Each notebook provides hands-on implementation with real-world datasets, detailed explanations, and best practices.

## 📂 Repository Structure

- **📁 Supervised Learning**  

  - Regression (Linear Regression, Decision Trees, etc.)  

  - Classification (Logistic Regression, SVM, Random Forest, etc.)  

- **📁 Unsupervised Learning**  

  - Clustering (K-Means, DBSCAN, Hierarchical Clustering)  

  - Dimensionality Reduction (PCA, t-SNE)  

- **📁 Semi-Supervised Learning**  

  - Self-training, Label Propagation  

- **📁 Reinforcement Learning**  

  - Q-Learning, Deep Q-Networks (DQN), Policy Gradient  

Each notebook is well-documented, with **code walkthroughs, visualizations, and theoretical insights** to help users understand and implement machine learning models effectively.

## 🚀 Getting Started

- Clone the repository:  

   ```bash

   git clone https://github.com/your-username/machine-learning-repo.git  

   ```

## 📜 Requirements

- Python 3.x  

- Scikit-Learn, TensorFlow, PyTorch  

- Pandas, NumPy, Matplotlib, Seaborn  

## 🎯 Target Audience

This repository is ideal for **beginners, students, and professionals** looking to learn or revise machine learning concepts with hands-on implementation.

## ⭐ Contributing

Feel free to contribute by adding new examples, improving existing code, or updating documentation.