dominikhommer / machinelearning

Discover the power of Machine Learning through practical projects in our dynamic repository. Dive into linear models, classification techniques, and more, with projects ranging from spam detection to fruit classification. Perfect for learners at all levels, our repository grows with new insights and applications. Stay tuned for continuous updates!

Machine Learning Course Projects

Welcome to my Machine Learning repository! This repository contains a collection of Jupyter notebooks (.ipynb files) that showcase the projects and exercises I've completed as part of my Machine Learning course. Each notebook is a comprehensive guide through the concepts and applications I've learned, designed to demonstrate my understanding and skills in the field of Machine Learning.

Overview 🚧

The content of this repository is organized into several directories, each representing a key area of Machine Learning that we've explored. Currently available for viewing are:

• Linear_Models: Notebooks in this directory focus on linear regression, logistic regression, and other linear models for both regression and classification tasks. It includes detailed explorations of various linear approaches in machine learning, showcasing their application on real-world datasets.

• Classification: This directory features notebooks that explore various classification algorithms beyond linear models, such as kNN, support vector machines (SVMs) etc. Projects within this directory demonstrate the classification of different datasets, ranging from fruit classification to spam email detection, using advanced machine learning techniques.

While the above directories are currently prepared and available for GitHub, the repository is a work in progress 🚧. More content is stored locally, awaiting preparation for GitHub. In the future, the repository may expand to include a variety of projects, not limited to but potentially involving:

• Projects utilizing for machine learning experiments and demonstrations.
• Further exploration into advanced classification and regression models, providing deeper insights into predictive analytics.
• Broadening the scope to include various machine learning techniques and their practical applications across different domains.

The aim is to gradually release more projects as they are prepared for GitHub, offering a comprehensive look at machine learning's diverse techniques and applications. Stay tuned for future updates and new additions to the repository.

Getting Started

To get started with these notebooks, you'll need to have Jupyter installed on your machine. You can install Jupyter using Anaconda or with pip:

pip install notebook

Once Jupyter is installed, you can run the following command in the terminal at the root of this repository to start Jupyter Notebook:

jupyter notebook

This will open a new browser window or tab displaying the Jupyter Dashboard, from where you can navigate to and open any notebook in this repository.

Objectives

The main objectives of this repository are to:

1. Document my learning progress and understanding of Machine Learning concepts.
2. Serve as a portfolio of my work for future reference and for sharing with potential employers or collaborators.
3. Provide a resource for others learning Machine Learning, offering practical examples and insights.

Technologies Used

• Python 3
• Jupyter Notebook
• Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow/Keras

Contributing

While this repository primarily serves as a personal portfolio of my Machine Learning journey, contributions or suggestions for improvement are welcome. If you have any advice or ideas to enhance the content of this repository, feel free to open an issue or submit a pull request.

License

This project is open source and available under the MIT License.

Contact

If you have any questions or would like to connect, please feel free to reach out to me.

Thank you for visiting my Machine Learning repository!