A minimal and educational repository implementing core Machine Learning algorithms from scratch — no built-in ML libraries used.
This project is designed for learning and understanding the underlying logic, intuition, and mathematics behind machine learning models. It avoids the use of libraries like scikit-learn, xgboost, or lightgbm for model building, and focuses on building everything from the ground up using pure Python and NumPy.
- Implementations of core ML algorithms from scratch
- Focus on interpretability and step-by-step logic
- Uses only Python and NumPy
- Great for beginners who want to understand how ML works under the hood
- Linear Regression
- Ridge Regression
- K-Nearest Neighbors
- Logistic Regression
- Gaussian Determinant Analysis (GDA)
- Support Vector Machine (SVM)
- K-Nearest Neighbors
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Clone the repository:
git clone https://github.com/anshul-dying/ML-Algos-From-Scratch.git cd ML-Algos-From-Scratch -
Install Dependencies:
pip install pandas, numpy, matplotlib, scikit-learn, seaborn
- Learn core ML principles by implementing models manually
- Build mathematical and algorithmic intuition
- Avoid abstraction-heavy libraries until the fundamentals are solid
Contributions are welcome! If you want to:
- Add a new algorithm
- Improve code readability or efficiency
- Fix bugs or incorrect math Please open a pull request or create an issue.
Made with curiosity by Anshul