Python from Zero to Hero Part 2

This course takes off where the Maastricht Summer School “Python from Zero to Hero Part 1” ends: turning raw text into meaningful data, or more broadly ‘text mining’.
The goal of text mining is to uncover hidden patterns and/or relationships in large volumes of text data. In Python, text mining is typically done with the help of Machine Learning and Natural Language Processing (NLP) techniques.
In this summer school, participants will be introduced to the ins and outs of text mining with cutting-edge NLP methods. They will learn how to pre-process and wrangle text data so that it becomes machine-readable. Participants will also learn how to harness the capabilities of transformer-based Large Language Models (LLMs) in Python, such as Google’s BERT, for topic modelling and sentiment analysis.

Course leader

Dr. Arnoud Wils

Target group

This course is the first module of a Maastricht Summer School series on Python for (scientific) research. It organically prepares participants for the skills and knowledge taught in the part 2 course. Both modules can be followed independently.

In case participants wish to follow only this first part of the series, no prior knowledge of the Python Programming Language is required. Participants would benefit from prior experience in research skills and basis knowledge.

Course aim

• Develop an understanding of how computers can learn to understand text.
• Learn the ins and outs of large language models, from basic bag-of-words to more advanced transformer models.
• Learn to pre-process and wrangle text data.
• Provide hands-on experience in implementing text mining techniques (sentiment labelling – topic analysis) using popular Python libraries and frameworks.
• Outreach to generative AI models, augmenting LLMs with your custom data (how to chat with your documents?), and text-to-image models using LangChain and Huggingface models.