Machine Learning Algorithms From Scratch

A collection of Machine Learning algorithms implemented from scratch using Python and NumPy.

The goal of this repository is to build a deep understanding of how machine learning algorithms work internally by implementing them without relying on high-level ML libraries such as Scikit-Learn.

🚀 Objectives

• Understand the mathematics behind machine learning algorithms.
• Learn optimization techniques such as Gradient Descent.
• Build intuition for model training and parameter updates.
• Create a strong foundation for advanced Machine Learning and AI Systems.

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📚 Implemented Algorithms

Linear Models

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ordinary Least Squares (OLS)

Regularization Techniques

• Ridge Regression
• Simple Ridge Regression
• Ridge Regression using Gradient Descent
• Lasso Regression
• Elastic Net Regression
• Lasso + Elastic Net Regression

Classification Models

• Logistic Regression
• Perceptron
• K-Nearest Neighbors (KNN)
• Linear Support Vector Machine (Linear SVM)
• Sigmoid Function Implementation
• Decision Tree Classifier
• Random forest classifier

Optimization Algorithms

• Simple Gradient Descent
• Batch Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent

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🧠 Concepts Covered

• Cost Functions
• Loss Functions
• Gradient Descent
• Regularization
• Feature Scaling
• Distance Metrics
• Decision Boundaries
• Margin Maximization
• Optimization Techniques

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🛠 Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook / Python Scripts

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🎯 Why This Repository?

I am the kind of person who enjoys understanding what happens underneath the layers rather than only using frameworks and libraries.

This repository focuses on:

• Mathematical intuition
• Algorithm implementation
• Optimization techniques
• Learning by building
• Understanding models from first principles

Every implementation is written from scratch to strengthen core Machine Learning fundamentals.

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🤝 Contributions

Suggestions, improvements, and discussions are welcome.

If you find a bug or have an idea for improvement, feel free to open an issue or submit a pull request.

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⭐ Support

If you found this repository useful, consider giving it a star.

It helps others discover the project and motivates future development.

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📜 License

This project is open-source and available under the MIT License.

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📜 License

This project is open-source and available under the MIT License.