What is NLP in machine learning

Basics of text data analysis with NLP and machine learning (introductory course)

The challenge: "digging" for relevant information in digital test collections

Machine learning (ML) methods are available for you to analyze digital text data. These must be appropriately selected and adapted. Additional difficulties here are often the unstructured nature of the text and the lack of metadata.


The solution: Natural Language Processing (NLP) and machine learning (ML) to derive relevant information from texts!

You will learn methods of digital text analysis, with a focus on Natural Language Processing (NLP) and ML. This includes the entire processing pipeline for (possibly large) amounts of text.

To do this, you will first get to know the theoretical basics and Python methods of NLP, for example for crawling the Internet or machine-readable "cleaning up" and preprocessing (often unstructured) text data. Then, using programming examples, you will learn how well-known standard methods of ML (e.g. clustering, classification) work.

You will also see how you interpret the origin of the ML results and which statistical methods you can use to assess the quality of ML methods for text data analysis.

This course consists on the one hand of live lectures and demos as an online seminar.

In addition, we have prepared a number of Python programming tasks for you as Jupyter notebooks, which you can work on under the live supervision of our experts.

Of course we plan enough time for discussions, your questions and breaks.

For follow-up work after the end of the course, our experts are available for one more day via the internet forum to answer your questions.


Your benefits at a glance

After the seminar you can ...

  • understand which NLP and ML methods are available for your projects
  • implement these methods in Python
  • Apply and evaluate your implementation in your own projects

This seminar offers you ...

  • Mediation of current methods and tools for text data analysis
  • Findings from the current state of research on innovative methods in NLP and ML
  • Supervision in the processing of the Python programming examples by our experts in the video chat and via the Internet forum
  • Time for self-reflection and asynchronous learning to suit your learning pace, because our experts are available to answer questions via the forum after the seminar
  • Tips for literature and external sources of information with which you can keep your finger on the pulse after this course
  • Exchange with experts and networking with other users beyond our course