ML-From-Scratch is dedicated to providing clear and educational implementations of classic machine learning algorithms using Python, without relying on the advanced frameworks. This repository is ideal for those who wish to understand the inner workings of algorithms such as linear regression, logistic regression, and Naive Bayes classifiers from the ground up.

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

• Linear Regression: Used for predicting real-valued outputs.
• Logistic Regression: Suitable for binary classification tasks.
• Naive Bayes Classifier: A probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.
• K-Means Clustering: A method of vector quantization.
• (Include other algorithms as applicable)

Ensure you have Python 3.x installed on your machine. You can download Python from python.org.

Clone this repository to your local machine to get started:

git clone https://github.com/youneskazemi/ML-From-Scratch.git
cd ML-From-Scratch

Navigate into the specific algorithm directory you are interested in, and run the Python script. For example:

cd linear_regression
python linear_regression.py

Contributions to improve or add new algorithms are warmly welcomed. Please feel free to fork the repository, make changes, and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

This project is licensed under the MIT License - see the LICENSE file for details.