Machine Learning Basics

This collection contains open-source code implementations of fundamental methods used in machine learning.

Table of Contents

1. Introduction
2. Usage
3. Code Structure
4. Implemented Methods
   • Gradient Descent
   • K-Nearest Neighbors (KNN)
   • Naive Bayes
   • Perceptron
5. Contributing
6. License

Introduction

This repository aims to provide a comprehensive set of open-source implementations for basic machine learning methods. Whether you're a beginner looking to understand the fundamentals or an experienced practitioner wanting to revisit the basics, you'll find code examples and explanations here.

Usage

Feel free to explore the code in this repository for educational purposes, experimentation, or integration into your projects. Each directory corresponds to a specific machine learning method, containing the necessary code and accompanying documentation.

Code Structure

The code is organized into directories based on different machine learning methods. Each directory contains a README file with specific instructions and explanations related to that method.

Implemented Methods

Gradient Descent

Gradient Descent is a fundamental optimization algorithm widely used in machine learning for finding the minimum of a function.

K-Nearest Neighbors (KNN)

[K-Nearest Neighbors (KNN)](K-Nearest Neighbors (KNN)/README.md) is a simple and effective classification and regression algorithm based on the idea of similarity.

Naive Bayes

Naive Bayes is a probabilistic algorithm commonly used for classification tasks, especially in natural language processing.

Perceptron

Perceptron is a basic building block of neural networks and serves as a binary classifier.

Contributing

If you'd like to contribute to this repository by adding new methods, improving existing code, or fixing bugs, please message me.