IKILeUS: Natural Language Processing

Businesses today have large amounts of voice and text data collected from different channels such as emails, news, calls, and customer reviews. These data can be used in companies to extract hidden patterns or key features and hence need to be efficiently processed and modelled using Natural Language Processing (NLP) algorithms.

The aim of this course is to provide basic knowledge of NLP technology, which has recently gained special attention in various fields such as marketing, customer service and e-commerce. This course covers different algorithms and topics, including sentiment analysis, topic modeling, semantic search, chatbots, and transformers. In addition to key NLP techniques such as word embedding and LLM fine-tuning.  All methods and algorithms discussed in this course are explained with figures and diagrams, as well as practical examples to show you how you can apply these methods for your own business.


Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

May 21, 2024

End date

Jun 14, 2024



Entry level


Course subject areas

Data in HPC / Deep Learning / Machine Learning

Supercomputing Academy


Artificial Intelligence

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Prerequisites and content levels

  • Good experience in Python programming.
  • Basic knowledge in machine learning.
  • Basic knowledge in Linux.
Content levels

Community level: 40 hours

Target audience

This course is intended for, but is not limited to, the following groups:

  • Postgraduate non-computer scientists (e.g. engineers).
  • Research and business staff working with text and voice data.  
  • Everyone who is interested in NLP.

Learning outcomes

After this course, participants will:

  • gain basic knowledge of Natural Language Processing technology.
  • learn about terminology and algorithms of Natural Language Processing.  
  • learn methods for data preprocessing, data cleaning and data preparation.
  • learn how to build NLP models using Python.
  • learn about various methods and tools for interpreting and visualizing the output of the models.
  • deepen your understanding of various models and algorithms using tasks and quizzes.


Layal Ali (HLRS) layal.ali(at)hlrs.de


  • Week 1: Introduction, Word Vectorization Techniques, Term Frequency- Inverse Document Frequency (TF-IDF), Named Entity Recognition (NER).
  • Week 2: Speech Recognition, Text Generation, Topic Modeling, Sentiment Analysis.
  • Week 3: Semantic Search, Text Summarization, Next Word Prediction.
  • Week 4: Chatbots, Transformers, LLM Fine-Tuning.

You can proceed at a self-paced manner, but the seminars will address those topics in the specified order.


Register via the button at the top of this page.
We encourage you to register to the waiting list if the course is full. Places might become available.

Registration closes on May 12, 2024.


This course is free of charge.

HLRS concept for flexible learning

Flexible Learning

This course offers flexible learning, allowing you to learn at your own pace and access online course materials and cluster resources. Web-seminars are held weekly to discuss the learning modules and to answer your questions. We also provide forum channels that enable you to communicate with the lecturer and peers, as well as to share your experiences.

Learning Duration

The course is divided into four learning units of 10 hours each. Participants can learn the individual learning content on their own schedule. In addition, this course has fixed dates for virtual seminars and the exam. In total, the course lasts up to 40 learning hours.

Certificate & Attendance Confirmation

High-Performance Computing Center (HLRS) issues participants an attendance confirmation if they have attended all seminars, as well as a certificate if they have passed the exam at the end of the course.

Technical Requirement
  • Stable Internet connection so you can access and download the learning materials.
  • Access to video conferencing tool with camera and microphone for participation in regular seminars.


Tibor Doepper, phone 0711 685 87233, training(at)hlrs.de


This course is offered within the framework of the project “Integrated Artificial Intelligence in Teaching at the University of Stuttgart” (IKILeUS). The aim of IKILeUS is to provide students with a comprehensive and sustainable understanding in the field of artificial intelligence (AI) and to introduce AI-based technologies that can improve teaching at the University of Stuttgart.

HLRS Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.

Further courses

See the training overview and the Supercomputing Academy pages.

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