Machine Learning with Python
virtual course


Datum: Date 26 October 2021 (1:00pm - 5:15pm)
02 November 2021 (1:00pm - 5:15pm)
09 November 2021 (1:00pm - 5:15pm)
16 November 2021 (1:00pm - 5:15pm)
Kurssprache / Language English
Leistungspunkte / Credit Points 0,5
Arbeitseinheiten (AE) HDS-Zertifikat / work unit (AE) HDS Certificate 20
Anmelde­schluss: Registration Deadline 19. Oktober 2021
Der Anmeldeschluss ist überschritten. Registration Deadline has been exceeded.

Course description:

Machine Learning can be used to derive knowledge from data sets. For this purpose, algorithms allow to recognize patterns in data. In this course, we will learn about the workflow in a Machine Learning project. Further, different algorithms will be introduced. We will apply this knowledge in practice by working on real-world use cases with the programming language python.

Recommendation / Prerequisite:

  • Basic Python with focus on pandas and seaborn
  • Jupyter Notebook
  • e.g. Course offered by Segun Ajibola (Python, visualization etc.)
1. Workshop:
  • Introdutcion to Machine Learning
  • Workflow in an ML project
  • Focus on supervised learning algorithms (distance based, tree-based algorithms, neural networks etc.)
  • Dimension reduction (such as feature selection)
  • Hyperparameter optimization
2. Structure/ Methods:
  • Theoretical Introduction
  • Live Coding (screen-sharing: participants follow on their own computer)
  • Practical training session (participants work in smal groups for excersises)
In addition, individual advisory service is offered.
Learning Outcome:
  • know the basic procedures in a machine learning project
  • be able to implement ML projects yourself
Bianca Huber works as a freelance Data Scientist. Besides project specific implementation, she regularly trains different groups on topics related to machine learning with a focus on Python and R. This includes courses for the university sector up to tailored employee trainings of industry partners.
Please note, that we can only give certificates to thos who participated at least in 80% of the course time.