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🚀 Machine Learning Algorithms from Scratch

Welcome to my repository where I code Machine Learning Algorithms from scratch using only Python and Numpy — no other external libraries are used. This project aims to deeply understand how various ML algorithms work under the hood by breaking them down into their core implementations.

🛠️ What's Inside

This repo covers a wide range of supervised and unsupervised machine learning algorithms, deep learning concepts and is updated frequently.

📚 Learning Goals

This repo is designed to:

  • Provide a deeper understanding of the math behind machine learning algorithms.
  • Reinforce programming skills by implementing algorithms without relying on libraries.
  • Enable you to customize ML algorithms for specific needs.

🧑‍💻 Prerequisites

To follow along, you’ll need:

  • Python 3.7 or higher.
  • Basic knowledge of Linear Algebra, Calculus, and Probability.

🚀 Getting Started

  1. Clone the repository:

    git clone https://github.com/hardiknahata/ml-algorithms-from-scratch.git
cd ml-algorithms-from-scratch

  2. Run any algorithm, example:

    python linear_regression/train.py

📝 Documentation

Each folder contains:

  • A Python implementation of the algorithm.
  • A README file detailing the theory behind the algorithm.

🤝 Contributions

Feel free to open issues or submit pull requests if you have improvements or new algorithms to add!

📬 Contact

If you have any questions or want to discuss the project, you can reach me via: