Machine Learning

A comprehensive collection of machine learning algorithms implemented from scratch and demonstrated with practical examples. The project covers core ML topics — from linear regression to neural networks — organized into four thematic blocks.

Table of Contents

• Machine Learning
  • Table of Contents
  • Overview
  • Labs
  • Documentation
  • Contributing
  • License

Overview

This project demonstrates practical competencies in fundamental machine learning algorithms. Each block focuses on a specific family of methods with self-contained Jupyter notebooks and/or Python scripts that include theory references, implementation details, visualizations, and evaluation metrics.

Labs

• Linear Regression
  • Constructing a Regression Line (link)
• Decision Trees
  • Classification Tree and Random Forest (link)
  • Decision Trees - Regressor (link)
• Classification
  • Naive Bayes (link)
  • Neighborhood Alignment (link)
  • Support Vector Machines (SVM) (link)
• Clustering
  • k-Means, k-Means++ (link)
  • DBSCAN (link)
  • Mean Shift (link)
• Dimension Reduction Algorithms
  • Principal Component Analysis (PCA) (link)
• Neural Networks
  • Feedforward Neural Network, Classification (link)
  • Feedforward Neural Network, Regressor (link)

Documentation

Full documentation is available at ptrvsrg.github.io/machine-learning.

Contributing

Contributions are welcome! Please read the Contributing Guide and the Code of Conduct before getting started.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

Distributed under the terms of the license specified in the LICENSE file.