In the continuing education course to become a Specialist in Natural Language Processing (NLP), you learn how to develop and apply technologies for processing natural language. The seminar covers central topics such as data preprocessing, text corpora, vectorization, word embedding, language models, text classification, and keyword extraction. Furthermore, information extraction and machine translation as well as the use of large language models and their improvement through human feedback are discussed. Transfer learning teaches how existing models can be adapted to new tasks. An additional focus is on legal frameworks in the EU, including the AI Act.
This continuing education course is particularly relevant because applications like ChatGPT have made the potential of NLP technologies more clearly visible. As a Specialist in NLP, you can develop pioneering solutions for processing natural language and integrate them into products and business processes. This knowledge opens up new career opportunities not only for data scientists but also for product managers and technical leaders. People with a background in computer science or linguistics can further sharpen their academic and professional profile through this training. The course is especially exciting for lawyers and ethicists who need to familiarize themselves with the regulatory and ethical challenges of machine language processing and EU regulations.
The advantages of the continuing education course lie in the broad applicability of the skills in various professional fields. The demand for specialists who are able to handle the complex technology of NLP is growing steadily. Furthermore, the course offers the opportunity to stay up to date with the latest technology and understand current developments such as the AI Act, which is particularly relevant for activities in the EU.
Interest in language processing, technology, and their regulatory aspects is helpful to get the most out of this continuing education course. Participants should bring a certain affinity and understanding of technical contexts in order to effectively apply complex models and algorithms.
