Welcome to the ML-From-Scratch repository, a meticulously crafted collection of machine learning algorithms implemented from the ground up using Python and NumPy. This project is designed to serve as an educational resource, providing clear insights into the foundational mechanics of various machine learning models without the abstraction of high-level libraries.
The primary objective of this project is to demystify the core principles of machine learning by offering transparent implementations of widely used algorithms. By constructing these algorithms from scratch, we aim to enhance understanding and provide a solid foundation for further exploration in the field of machine learning.
This repository encompasses a diverse range of machine learning algorithms, including but not limited to:
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Supervised Learning:
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines
- K-Nearest Neighbors
- Neural Networks
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Unsupervised Learning:
- K-Means Clustering
- Principal Component Analysis
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Reinforcement Learning:
- Q-Learning
Each algorithm is implemented with a focus on clarity and educational value, emphasizing the underlying mathematics and logic that drive these models.
To explore and utilize the algorithms in this repository, follow these steps:
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Clone the Repository:
git clone https://github.com/ahammadnafiz/ML-From-Scratch.git
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Navigate to the Project Directory:
cd ML-From-Scratch -
Set Up a Virtual Environment (Optional but Recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install the Required Dependencies:
pip install -r requirements.txt
Each algorithm is contained within its respective directory and includes a Python script demonstrating its functionality. To run an algorithm:
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Navigate to the Linear Regression Directory:
cd ML from Scratch/Linear Regression # Example for Linear Regression
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Execute the Implementation Script:
python Linear_Regression.py
These scripts are designed to be self-explanatory, with comments and documentation to guide you through the implementation details.
We welcome contributions from the community to enhance the quality and breadth of this project. If you wish to contribute:
- Fork the Repository
- Create a New Branch:
git checkout -b feature/YourFeatureName
- Commit Your Changes:
git commit -m 'Add some feature' - Push to the Branch:
git push origin feature/YourFeatureName
- Open a Pull Request
Please ensure that your contributions align with the project's objectives and maintain the focus on educational clarity.
This project is licensed under the MIT License. For more details, please refer to the LICENSE file.
This repository is dedicated to learners and practitioners seeking a deeper understanding of machine learning algorithms through hands-on implementation.