Welcome to the Machine Learning From Scratch repository! Here you'll find implementations of fundamental machine learning algorithms written entirely in Python without external libraries. The purpose of this project is to help you understand the inner workings of machine learning models by coding them from the ground up.
- Linear Regression
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Decision Trees
- Neural Networks (Perceptron, Multi-layer Perceptron)
- Clustering (k-Means)
- Dimensionality Reduction (PCA)
Each algorithm is organized into its own Python file with accompanying explanations and examples. To use any of these implementations, navigate to the respective file and run it in your Python environment.
Contributions to this repository are welcome! Please submit a pull request if you'd like to contribute to a new algorithm implementation, improve existing code, or fix any issues.
Happy learning and coding!