The ML From Scratch

Explore fundamental machine learning algorithms with the_ml_from_scratch. This project provides hands-on implementations using numpy, ideal for beginners and enthusiasts to build a solid understanding of core concepts.

Table of Contents

• Introduction
• Folder Structure
• Installation
• Usage
• Contributing
• License

Introduction

the_ml_from_scratch is designed to help you understand the basics of machine learning algorithms through hands-on implementations using numpy. The project is structured to provide clear and comprehensive examples of various ML techniques.

Folder Structure

the_ml_from_scratch/
├── 01_Regression/
│   ├── LogisticRegression/
│   │   ├── LogisticRegression.py
│   │   ├── model_training.py
│   ├── SimpleLinearRegression/
│   │   ├── LinearRegression.py
│   │   ├── model_training.py
├── 02_Classification/
│   ├── DecisionTree/
│   │   ├── DecisionTree.py
│   │   ├── model_training.py
│   ├── KNN/
│   │   ├── KNN.py
│   │   ├── model_training.py
│   ├── NaiveBayes/
│   │   ├── NaiveBayes.py
│   ├── RandomForest/
│   │   ├── DecisionTree.py
│   │   ├── RandomForest.py
│   │   ├── model_training.py
│   ├── SVM/
│   │   ├── svm.py
├── 03_Clustering/
│   ├── KMeans/
│   │   ├── kmeans.py
│   ├── PCA/
│   │   ├── PCA.py
├── .gitignore
├── README.md

Installation

To get started with the_ml_from_scratch, follow these steps:

1. Clone the repository

git clone https://github.com/PujanMotiwala/the_ml_from_scratch.git

2. Navigate to the project director

cd the_ml_from_scratch

3. Install the required dependencies

pip install -r requirements.txt

Usage

Explore the various folders for different types of machine learning algorithms. Each folder contains Python scripts that implement specific algorithms from scratch.

Contributing

We welcome contributions from the community! If you have an idea for a new algorithm, improvement, or bug fix, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.