Welcome to the Machine Learning and Deep Learning Basics repository! This repository contains a collection of basic machine learning and deep learning codes, designed to provide a foundational understanding of these fields.
This repository serves as a starting point for beginners in machine learning and deep learning. It includes simple and well-commented examples to help you understand the core concepts and algorithms. The repository covers:
- Basic machine learning algorithms (e.g., linear regression, logistic regression, decision trees)
- Fundamental deep learning models (e.g., neural networks, convolutional neural networks, recurrent neural networks)
- Data preprocessing techniques
- Model evaluation and performance metrics
To run the codes in this repository, you need to have Python installed. Follow these steps to set up your environment:
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Clone the repository:
git clone https://github.com/AMevans12/Machine-Learning.git cd Machine-Learning -
Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the required packages:
pip install -r requirements.txt
You can run the scripts directly from the command line or explore the examples in the Jupyter notebooks. Here are a few examples:
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To run the linear regression example:
python ml/linear_regression.py
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To open the machine learning examples notebook:
jupyter notebook notebooks/ml_examples.ipynb
Contributions are welcome! If you have any improvements, suggestions, or bug fixes, please create an issue or submit a pull request. Please ensure your contributions adhere to the following guidelines:
- Fork the repository and create your branch from
main. - Ensure your code is well-documented and tested.
- Follow the existing coding style.
If you have any questions or suggestions, feel free to reach out:
- Email: [email protected]
- GitHub: AMevans12
Happy coding!