Machine Learning Algorithms From Scratch 🤖

A comprehensive collection of machine learning algorithms implemented from scratch in Python for educational purposes. This repository aims to provide clear, well-documented implementations to help understand the inner workings of fundamental ML algorithms.

🎯 Purpose

This project serves multiple purposes:

• Deep understanding of machine learning algorithms by implementing them from first principles
• Educational resource for others learning ML
• Reference implementation for common ML algorithms
• Practice ground for Python programming and ML concepts

Each implementation includes:

• Detailed mathematical explanations
• Step-by-step implementation walkthrough
• Example usage and test cases
• Performance comparisons with sklearn implementations

✅ Implemented Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines
• Ridge Regression
• Lasso Regression
• K-Nearest Neighbors
• Naive Bayes
• XGBoost
• Multi-layer Perceptron Regressor
• Multi-layer Perceptron Classifier
• Elastic Net Regression
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Principal Component Analysis
• t-SNE
• Convolutional Neural Networks (basic convolution operations, pooling layers and LeNet-5)
• Recurrent Neural Networks (basic RNN, LSTM)
• Transformers
• Autoencoders
• Variational Autoencoders
• Graph Neural Networks

⚠️ Note

This repository is actively maintained and updated. Star the repository to stay updated with new implementations!