ML ALGORITHMS FROM SCRATCH

Description

This project aims to help you learn how classic machine learning algorithms are implemented from scratch using only NumPy. By doing so, you will gain a deeper understanding of the underlying mechanics of these algorithms.

Implementations

• Linear Regression
• Logistic Regression
• Decision Tree
• K-Nearest Neighbors
• Naive Bayes
• Perceptron
• Random Forest
• Support Vector Machine
• Linear Discriminant Analysis
• Principal Component Analysis
• K-Means Clustering

Installation

git clone https://github.com/yourusername/ML-ALGORITHMS-FROM-SCRATCH.git

cd ML-ALGORITHMS-FROM-SCRATCH

To install dependencies and set PYTHONPATH, install setup.py.

pip install -e .

License

This project is licensed under the MIT License.