Overview
The ML-AI-Algorithms-from-scratch repository provides implementations of several machine learning algorithms, including supervised, unsupervised, Bayesian, neural networks, and reinforcement learning algorithms. All implementations are done from scratch using only numpy and basic libraries, making it an excellent resource for those looking to understand the underlying mechanics of these algorithms without relying on high-level libraries.
Key Features
- Comprehensive Coverage: Includes a variety of algorithms across different categories of machine learning.
- Educational Resource: Ideal for students and practitioners who want to learn how algorithms work internally.
- Numpy-Based: Utilizes numpy for efficient numerical computations, ensuring performance while maintaining simplicity.
Use Cases
- Learning Tool: Perfect for educational purposes, helping users grasp the fundamentals of machine learning.
- Research: Useful for researchers looking to experiment with algorithm modifications or enhancements.
- Prototyping: A great starting point for developers wanting to build custom machine learning solutions without the overhead of complex libraries.