Welcome to the ML-Algorithms-from-scratch repository! This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Understanding the inner workings of these algorithms is crucial for becoming proficient in machine learning. This repository contains implementations of various machine learning algorithms from scratch using Python.
- Linear Regression (will be implemented)
- Logistic Regression (will be implemented)
- K-Nearest Neighbors (KNN) (will be implemented)
- Decision Tree (will be implemented)
- Random Forest (will be implemented)
- Support Vector Machine (SVM) (will be implemented)
- K-Means Clustering (will be implemented)
- Principal Component Analysis (PCA) (will be implemented)
- Neural Networks (will be implemented)
- (More algorithms will be added...)
Use the Installation guide provided in the each project.
ML-Algorithms-From-Scratch
Use each project using the installation guide provided in the project's folder.
Each algorithm has its own dedicated folder containing the implementation and a Jupyter notebook with examples of how to use it. To run the notebooks, you need to have Jupyter installed:
pip install jupyter jupyter notebook
Open the notebook you are interested in and run the cells to see the algorithm in action.
Contributions are welcome! If you have any suggestions, improvements, or new algorithms to add, please feel free to create a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for details.