michaelkernaghan / machine-learning-lectures

Lectures on Machine Learning, Large Language Models, and so forth

Machine Learning explorations Website

This repository hosts the web content and backend scripts for a series of educational explorations on Machine Learning (ML) and Artificial Intelligence (AI). The website aims to provide insightful explorations ranging from introductory topics to advanced discussions on ML techniques, including practical examples implemented through Python scripts.

Features

• Educational Content: Detailed HTML pages for each exploration covering various topics in machine learning and AI.
• Interactive Examples: Use of Python scripts to demonstrate practical applications like generating dog names using bigrams.
• Responsive Design: The website is designed to be fully responsive, providing a good user experience across different devices.

Repository Structure

• /scripts: Python scripts that demonstrate machine learning concepts, such as the bigram name generator.
• /styles: CSS files for styling the website.
• /images: Directory containing all images used in the explorations.
• README.md: This file, providing an overview of the project and guidance on how to use it.

Installation

To get the website running locally or deploy it:

Prerequisites

• Web browser to view the HTML files.
• Python 3 if running the included Python scripts.

Running the Website

1. Clone this repository to your local machine.
2. Open the HTML file index.html with a web browser to access the explorations.
3. Python scripts in the /scripts directory can be run separately to see machine learning concepts in action.

Running Python Scripts

• Navigate to the /scripts directory.
• Run a script using:

  python dog_name_predictor.py

Usage

• Educators and Students: Use the website as a teaching or learning tool for machine learning courses.
• Developers and Enthusiasts: Explore the Python scripts to understand practical implementations of ML concepts.

Contributing

Contributions are welcome! If you have suggestions to improve the content, fix issues, or enhance the functionality of the Python scripts, please feel free to fork the repository and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.