ML-Deep-Learning-Algorithms

Welcome to the ML-Deep-Learning-Algorithms repository! This is your one-stop destination for a collection of powerful machine learning and deep learning algorithms implemented in Python.

Table of Contents

1. About
2. Algorithms
3. Installation
4. Getting Started

About

This repository is a curated collection of popular machine learning and deep learning algorithms.

Features

• Compatible with popular machine learning libraries such as TensorFlow, and scikit-learn.
• Continuous updates and improvements.

Algorithms

Here's a list of the algorithms currently included in this repository:

Regression

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Support Vector Machine (SVR)
5. Decision Trees and Random Forests Regression

Classification

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Support Vector Machine (SVC)
4. Naive Bayes Classification
5. Decision Trees and Random Forests Classification

Clustering

1. K-Means Clustering
2. Hierarchical Clustering

Association Rule Learning

1. Apriori

Reinforcement Learning

1. Upper Confidence Bound (UCB) Intuition

Each algorithm is organized into its own directory, containing Python code, and sample datasets where applicable for detailed explanations and usage.

Installation

To get started, you'll need Python 3.11 and a few common libraries. You can set up your environment using pip:

pip install -r requirements

Getting Started

To get started, clone this repository to your local machine using the following command:

git clone https://github.com/khedrmahmoud/ML-Deep-Learning-Algorithms.git

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