Machine Learning Algorithms from Scratch

An Undergraduate Research Minor

Welcome to the Machine Learning Algorithms from Scratch repository! This repository is dedicated to helping you understand the fundamental concepts of various machine learning algorithms by implementing them from scratch. Whether you're a beginner looking to dive into the world of machine learning or an experienced practitioner aiming to reinforce your understanding, this repository is a great place to start.

Currently under development. Will update as I progress

Table of Contents

• Introduction
• Algorithms
• Getting Started
• Contributing
• License

Introduction

In the field of machine learning, understanding the inner workings of algorithms is essential for building a strong foundation. By implementing algorithms from scratch, you gain insights into the mathematical principles that drive these methods and learn how to optimize and fine-tune them. This repository is designed to provide you with hands-on experience in building machine learning algorithms without relying on external libraries.

Algorithms

The repository is amied to cover all the basic machine learning algorithms, including but not limited to:

• Linear Regression
• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• Neural Networks
• K-Means Clustering
• Principal Component Analysis (PCA)
• Gradient Boosting

Each algorithm will have its own directory within the repository, containing the implementation code, necessary datasets (if applicable), and a README explaining the algorithm's theory and usage.

Getting Started

To get started with using this repository, follow these steps:

1. Clone the Repository: Begin by cloning this repository to your local machine using the following command:

2. Navigate to Algorithm Directory: Once you've cloned the repository, navigate to the specific algorithm directory you're interested in. Each algorithm will have its own separate directory with a self-explanatory name.

3. Explore the Code and Documentation: Inside the algorithm directory, you'll find the implementation code and a README file. The README file will provide you with an overview of the algorithm, its mathematical foundation, and instructions on how to run the code.

4. Run the Code: Follow the instructions in the README file to run the algorithm's implementation. You can experiment with different parameters, datasets, and settings to deepen your understanding.

Contributing

Contributions to this repository are more than welcome! If you find any bugs, want to optimize an algorithm, or want to add a new algorithm to the collection, feel free to create a pull request. Please ensure that your code is well-documented and follows the established structure.

If you're not comfortable with coding but still want to contribute, you can help by improving the documentation, fixing typos, or suggesting algorithm ideas.

License

This project is licensed under the MIT License. Feel free to use the code in this repository for educational and non-commercial purposes.

Start exploring the world of machine learning algorithms from scratch and have fun learning and experimenting! If you have any questions, feel free to open an issue or reach out to the repository maintainers.

Happy coding!