Welcome to the official repository for the "Introduction to Python Programming for Machine Learning and AI" (IPML) course, offered to bachelor students at Humboldt-Universität zu Berlin by the Information Systems Team. This repository serves as a comprehensive compilation of materials, including code samples, demonstrations, and more, which are integral to the tutorials accompanying the lecture series.
Should you be inclined towards a version of the course tailored for self-study, we encourage a visit to Quantinar.
For enrolled students, additional resources, updates, and discussions are available on our Moodle course page.
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├── data # Folder containing datasets used in tutorials
├── resources # Additional resources relevant to the course
├── tutorial_notebooks # Jupyter notebooks covering the tutorial contents
├── work_in_progress # Materials and drafts under development
├── LICENSE # License file for the repository
├── README.md # The file you are currently reading
├── ipml-env.yaml # Conda environment file for setting up the IPML workspace
└── requirements.txt # List of Python dependencies necessary for the tutorials
For instructions on how to use these materials, refer to the guides available in the resources folder. Here, you'll find comprehensive documentation on how to access and utilize the Humboldt University JupyterHub for cloud-based execution of the included Jupyter notebooks. Additionally, there's a guide for setting up a local Python environment should you prefer or require a local setup.
This project is licensed under the terms of the MIT License.