ML-from-scratch

This repository contains a collection of machine learning algorithms implemented from scratch using Python. The goal is to provide a deeper understanding of machine learning models by building them incrementally without relying on advanced machine learning libraries.

Features

• Implementations of core machine learning algorithms from the ground up
• A focus on understanding algorithmic details rather than using pre-built solutions
• Well-structured code with clear documentation and examples

Table of Contents

• ML-from-scratch
  • Features
  • Implemented Algorithms
    • Supervised Learning
    • Unsupervised Learning
    • Neural Networks
    • Optimization Techniques
    • Other Algorithms
  • Installation

Implemented Algorithms

Supervised Learning

• Linear Regression
• Polynomial Regression

Unsupervised Learning

Neural Networks

• Backpropagation

Optimization Techniques

• Gradient Descent
• Steepest Descent
• Conjugate Directions
• Conjugate Gradients
• Stochastic Gradient Descent
• Gradient Descent with Momentum
• Nesterov Accelerated Gradient
• Newton's Method
• Adam Optimization
• Batch Gradient Descent
• Mini-Batch Gradient Descent

Other Algorithms

• Eigen-Vectors and Eigen-Values

Installation

Clone the repository:

git clone https://github.com/robertpaulp/ML-from-scratch.git
cd ML-from-scratch

Ensure you have the required dependencies installed. You can install them using pip:

pip install -r requirements.txt