Machine Learning Labs Repository

Welcome to the Machine Learning Labs GitHub repository! This repository is a collection of resources and projects related to machine learning, designed to help you learn and experiment with various machine learning techniques, algorithms, and tools. This Project is solely based on the ICS 4102 Machine Learning Course by Strathmore University lectured by Mr Emmanuel Olang.

Table of Contents

1. Introduction
2. Getting Started
3. Projects
4. Contributing
5. License

Introduction

Machine learning is a rapidly evolving field with numerous applications in various domains, such as natural language processing, computer vision, reinforcement learning, and more. This repository aims to provide you with a hands-on experience in machine learning through practical projects and educational resources.

Getting Started

To get started with Machine Learning Labs, follow these steps:

1. Clone the Repository: You can clone this repository to your local machine using the following command:

git clone https://github.com/jeffvun/Machine-Learning-Labs.git

2. Set Up Your Environment: Depending on the project you're interested in, you might need to set up a specific Python environment with the required packages. Consult the project's README or documentation for details.

3. Explore the Projects: Navigate to the projects directory to find various machine learning projects, each in its dedicated folder. Each project folder contains a README with project-specific instructions and details.

4. Learn from the Resources: The resources directory contains educational materials, such as Jupyter notebooks, articles, and tutorials, to help you learn machine learning concepts and techniques.

5. To find out how good a poicy is follow the following link https://www.youtube.com/watch?v=KovN7WKI9Y0

Projects

Explore the following machine learning projects in this repository:

1. Image Classification: A project that demonstrates image classification using deep learning and convolutional neural networks (CNNs).

2. Natural Language Processing: A project focused on NLP tasks like sentiment analysis, text classification, and named entity recognition using popular NLP libraries.

3. Reinforcement Learning: Learn about reinforcement learning algorithms through practical examples, including classic control tasks and game playing.

4. Recommendation Systems: Build recommendation systems using collaborative filtering, matrix factorization, and deep learning approaches.

5. Anomaly Detection: Detect anomalies in time series data using various statistical and machine learning methods.

Contributing

We welcome contributions from the community to make this repository even more valuable for learners and practitioners of machine learning. If you'd like to contribute, please follow these guidelines:

1. Fork the repository to your GitHub account.
2. Create a new branch for your feature or bug fix.
3. Make your changes, commit them, and push to your fork.
4. Submit a pull request with a clear description of your changes.

Please review our Contributing Guidelines for more details.

License

This repository is licensed under the MIT License. Feel free to use the code and resources for your personal and educational purposes. We encourage you to provide attribution if you use this repository as a reference in your own projects.

Happy Machine Learning! 🚀🤖