Python: Introduction to Natural Language Processing (NLP)

Natural Language Processing (NLP) lies at the intersection of linguistics and artificial intelligence. It enables computers to understand human languages and retrieve meaning from their analysis. Applications of NLP can be found in Machine Translation, Sentiment Analysis, LLM Chatbots, Intelligent Systems, Content Analysis, Grammar correction etc. 
 
As a universal programming language, Python is used in a huge variety of application domains and is often used in data analysis tasks. For the analysis of textual data and especially in the interdisciplinary field of Natural Language Processing (NLP), Python is a very powerful tool.

This course is offered in collaboration with the Linguistic Research Infrastructure (LiRI) at UZH. 

Allgemeine Informationen

Dauer 12
  • Environment setup: Run Python code for NLP 
  • Pre-processing methods: data cleaning, tokenization, stop-word removal, normalization, stemming, lemmatization, sub-word representations 
  • Feature extraction: POS tagging, chunking, n-grams 
  • Semantic textual representations: Vector models, Word embeddings, Sentence embeddings 
  • Practical Applications: Keyword extraction, Sentiment analysis, Language Identification, Question-Answering 
  • Use the power of AI: LLMs as NLP tools 
APPB - Python Basics or equivalent knowledge is required. You should feel comfortable executing Python code, working on the command line, working with control structures, simple functions, and core data types in Python.
Students and employees of the University of Zurich.
By the end of the course, participants should:
  • have a good overview of the field of NLP and its methods and applications. 
  • be aware of and able to apply Python packages for NLP (e.g. SpaCy, Sentence-Transformers). 
  • be able to apply text pre-processing techniques for cleaning and preparing textual data. 
  • be able to identify and extract keywords in a text corpus. 
  • perform semantic and sentiment analysis. 
  • be able to interact with LLMs in zero-shot scenario. 
  • The course materials are going to be delivered throughout the course.  
  • The code snippets of each section will be delivered during the lesson. 
In this introductory course, students will explore the basics of text analytics and NLP with powerful Python packages (SpaCy, scikit-learn, fasttext, sentence-transformers). The course content is distributed over 12 hours through theoretical blocks and hands-on exercises in individual and pair work.
This course is offered in collaboration with the Linguistic Research Infrastructure (LiRI) at UZH.

Kursdaten

Code Leitung Daten Plätze frei Standort
FS26-ANLP-01 Schneider Gerold
Ellendorff Tilia
Kew Tannon 		Mi. 01. Juli 2026 (09:00 Uhr - 12:00 Uhr)
Fr. 03. Juli 2026 (09:00 Uhr - 12:00 Uhr)
Mi. 08. Juli 2026 (09:00 Uhr - 12:00 Uhr)
Fr. 10. Juli 2026 (09:00 Uhr - 12:00 Uhr)
Universität Zürich Irchel Die Kursanmeldung beginnt am 1. Februar für das Frühjahrssemester und am 1. September für das Herbstsemester.