Machine Learning Algorithms from Scratch

Welcome to my Machine Learning Algorithms from Scratch repository! This project contains foundational machine learning algorithms implemented in Python, focusing on building these models from the ground up to better understand their mechanics.

Project Overview

This repository currently includes implementations of linear regression and logistic regression. Building these from scratch helps develop an understanding of the mathematical concepts and coding techniques needed to create and optimize models.

Algorithms Included

• Linear Regression: Implemented using gradient descent to minimize the cost function for regression tasks.
• Logistic Regression: A binary classification algorithm using sigmoid activation and cross-entropy loss.
• Decision Tree: Splits data using feature-based rules to form a tree for classification or regression.
• Random Forest: Combines many decision trees trained on random data subsets to improve accuracy and reduce overfitting.

Getting Started

Prerequisites

• Python 3.13.0
• Basic knowledge of Python and machine learning fundamentals

Installation

Clone the repository:

git clone https://github.com/WaelYTz/ML_AlgorithmsFromScratch.git
cd ML_AlgorithmsFromScratch