Mastering Natural Language Processing with Python
Course Objective
Natural Language Processing (NLP) is a rapidly growing field that enables computers to understand, interpret, and generate human language. It is widely used in academic research across disciplines from digital humanities to social sciences and biomedical text analysis. This course focuses on advanced NLP techniques using the Python programming language, equipping researchers with the tools to analyze, model, and draw insights from textual data with clarity and precision. Through hands-on coding exercises and real-world datasets, you will develop practical skills for processing and analyzing natural language. You will explore foundational and advanced methods, from preprocessing and vectorization to machine learning models for text classification and generation.
Course Description
This course is designed for researchers and practitioners with prior experience in Python programming. It delves into core and advanced NLP techniques commonly applied in academic and research contexts.
After successfully completing this course, you will be able to:
- Process and clean raw textual data for analysis
- Transform text into numerical representations using techniques like TF-IDF and word embeddings
- Build and evaluate models for tasks such as sentiment analysis, topic modeling, and named entity recognition
- Use Python libraries such as NLTK, spaCy, scikit-learn, and Hugging Face Transformers
- Apply best practices for interpretability, reproducibility, and responsible use of NLP in research
Just following along fancy slides won’t magically transfer the skill of coding to you. But you actively engaging with the course content in your development environment will more likely do just that.
That’s why you need to prepare accordingly: Please ensure that you have access to Google Colab before the course. We will use Google Colab for the coding parts, such that we can use Python without (sometimes time-consuming) pre-configuration or installation on your machine. To use Google colab, you need a Google account (same account which is used for Gmail, YouTube, etc.)
Course Material
You can find the lecture slides here.
In case you want to look something up to apply it in the hands-on exercise sessions, you can find the lecture notebook here. But please be aware that this is just a quick overview and I will provide you with more examples during the lecture.
The Google Colab notebook with the exercises is here. The solutions are here.
Happy coding!