Overview
This repository contains implementations of various machine learning algorithms from scratch, written in Python 3.x. It serves as a practical guide for understanding the underlying principles of machine learning by providing clear and concise code examples.
Key Features
- Diverse Algorithms: Includes implementations of popular algorithms such as linear regression, logistic regression, decision trees, and more.
- Educational Resource: Ideal for students and professionals looking to deepen their understanding of machine learning concepts.
- Python 3.x Compatibility: All code is written in Python 3.x, ensuring compatibility with modern Python environments.
- Hands-On Learning: Encourages users to modify and experiment with the code to see how changes affect algorithm performance.
Use Cases
- Learning Tool: Perfect for those new to machine learning who want to see how algorithms work under the hood.
- Reference Material: A valuable resource for developers looking to implement machine learning algorithms in their projects without relying on libraries.