Machine Learning Algorithms from Scratch

This repository contains Python implementations of various machine learning algorithms from scratch using only native Python and NumPy.

Introduction

In this repository, I've implemented several popular machine learning algorithms using Python and NumPy, without relying on external libraries. The goal is to provide a clear and educational resource for understanding the internals of these algorithms.

Each algorithm is implemented in a separate Python file and is accompanied by detailed explanations and comments to aid comprehension. This repository is a great resource for learning how these algorithms work under the hood.

Algorithms

The implemented algorithms include:

• Linear Regression
• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees
• Support Vector Machines (SVM)
• K-Means Clustering
• Principal Component Analysis (PCA)
• Feed Forward Neural Network (MLP)
• ... (Much more will be added)

Getting Started

To use these implementations, follow these steps:

1. Clone this repository to your local machine using: git clone https://github.com/your-username/your-repo.git

2. Navigate to the specific algorithm's directory you're interested in: cd algorithm-name

3. Open the algorithm's Python file in your preferred editor to study the code and comments.

Usage

Each algorithm's implementation comes with a clear and concise example that demonstrates how to use it on a sample dataset. The examples are contained within the respective algorithm's Python file.

To run the example for a specific algorithm, simply execute its Python file using your Python interpreter.

Contributing

Contributions to this repository are welcome! If you find any bugs, errors, or improvements, please feel free to open an issue or submit a pull request. Your contributions can help improve the educational value of this resourc