jayanth-mkv / machinelearning-algorithms-from-scratch

MACHINELEARNING ALGORITHMS FROM SCRATCH

ML Algorithms from Scratch

Welcome to my repository containing machine learning algorithms written from scratch in Python! This repository is intended to serve as a resource for anyone looking to gain a deeper understanding of machine learning algorithms, as well as for those looking to implement them in their own projects.

Overview

This repository contains the following machine learning algorithms implemented from scratch:

• Linear Regression
• Gradient Descent
• Decision Trees
• K-Nearest Neighbors
• Naive Bayes
• Perceptron
• Multi-Layer-Perceptron
• Backpropagation
• K-Means Clustering

Each algorithm is implemented using Python and is accompanied by a detailed explanation of its workings, along with relevant code snippets and examples.

Usage

To use any of these algorithms in your own project, simply copy the relevant code from the corresponding Python file in this repository. You can then modify the code as needed to suit your specific use case.

To run the examples provided for each algorithm, simply navigate to the relevant Python file and execute it using Python.

Contributing

I welcome contributions from anyone looking to add new algorithms or improve existing ones. To contribute, simply fork this repository and submit a pull request with your changes.

Conclusion

I hope that this repository will serve as a useful resource for anyone looking to learn more about machine learning algorithms or implement them in their own projects. If you have any questions or feedback, please don't hesitate to reach out to me.