This repository provides bare-bones NumPy implementations of fundamental machine learning models and algorithms. It targets students, researchers, and developers seeking to understand the inner workings of ML concepts, offering accessible, transparent code over raw performance.

How It Works

The project focuses on clarity and educational value, implementing algorithms directly in NumPy. This approach avoids complex optimizations or external libraries, allowing users to trace the mathematical operations and data flow for each model, from basic linear regression to deep learning architectures like CNNs and GANs.

Quick Start & Requirements

• Install via pip install ML-From-Scratch or by cloning the repository and running python setup.py install.
• Requires Python and NumPy.
• Examples can be run directly from the command line (e.g., python mlfromscratch/examples/polynomial_regression.py).

Highlighted Details

• Covers a broad spectrum of ML algorithms: supervised, unsupervised, reinforcement learning, and deep learning.
• Includes implementations for CNNs, GANs, DBSCAN, RBMs, and neuroevolution.
• Demonstrates concepts like polynomial regression, association analysis (Apriori), and genetic algorithms.
• Provides detailed model summaries and training progress visualizations in example outputs.

Maintenance & Community

The project is maintained by Erik Lindernoren. Contact information (email, LinkedIn) is provided for inquiries and suggestions.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

The project explicitly states its goal is accessibility and transparency, not computational efficiency. Implementations may not be optimized for performance or scalability.