Enrolment options
This “Natural Language Processing” (NLP) self-paced course, presented by the German Research Center for Artificial Intelligence and Technische Universität Berlin, is intended to introduce language processing using machine learning and deep learning models. The course presents basic NLP concepts like text pre-processing and vectorization. It also covers practical NLP tasks
Who is this course for?
The course is aimed at learners who don't want to use NLP models as a 'black box' and want to gain intuition for problem solving
What will you learn?
By the end of this course you will be able to develop your application to process text for the task and domain of your interest, choose the best model that fits the problem, considering the main factors that affect the final outcome and evaluate and compare different NLP models
Topics overview: NLP, vector representation, machine learning and translation, different language models and named entity recognition
How much time do you need to invest? Completing the course should take approximately 12 weeks at six (6) hours per week
Prerequisites: It’s recommended that learners have basic knowledge in Python programming, machine learning and prior knowledge in computer science and math
Course Certificate: A course certificate will be generated on the successful completion of the final quiz
Course Developers: S Mohtaj and Dr E Avramidis from the German Research Centre for AI (DFKI) in cooperation with KI Campus. (This course has been adapted and modified in accordance with its open license.)
Course Contact: fairforward@giz.de
For technical questions, please reach out to the atingi support team via the help-desk on the top right or to atingi@giz.de
License: CC BY-SA 4.0
If you are interested in this topic, you might also find this course on atingi useful: Artificial Intelligence for All: Open AI Training Data for African Languages.