ML-Algorithms-From-Scratch

A comprehensive collection of machine learning algorithms implemented both from scratch and using popular libraries. Each implementation includes detailed explanations, mathematical concepts, and practical examples.

🎯 Project Goal

This repository aims to provide clear, well-documented implementations of machine learning algorithms to help understand their inner workings. Each algorithm is implemented twice:

1. From scratch using NumPy (to understand the core concepts)
2. Using popular libraries like scikit-learn (for practical applications)

🗂️ Algorithms Included

• Linear Regression

  • Methods:
    • Gradient Descent
    • Normal Equation
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression

• Gradient Descent

  • Batch Gradient Descent
  • Stochastic Gradient Descent

• Neural Networks

  • Neural Network from Scratch

• Decision Tree

• More algorithms coming soon:

  • Logistic Regression
  • Support Vector Machines
  • K-means Clustering
  • Naive Bayes
  • Dimensionality Reduction

📚 Features

• Detailed Jupyter notebooks with step-by-step explanations
• Mathematical concepts and formulas
• Visualizations of algorithm behavior
• Performance comparisons
• Real-world examples and use cases
• Comprehensive documentation

🛠️ Technologies Used

• Python 3.8+
• NumPy
• Matplotlib
• scikit-learn
• Jupyter Notebook

🚀 Getting Started

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

3. Navigate to any algorithm folder
4. Open the Jupyter notebooks to see implementations

📖 Learning Path

Each algorithm folder contains:

• Theoretical explanation
• Step-by-step implementation
• Visualization of results
• Practical examples
• Performance evaluation

🤝 Contributing

Contributions are welcome! Feel free to:

• Add new algorithms
• Improve existing implementations
• Add more examples
• Enhance documentation

📝 License

This project is licensed under the MIT License - see the LICENSE file for details..