Machine Learning Workshop

Data science and machine learning are no exception: gray theory is no substitute for practical experience. Data handling and analysis are best learned in their application. By making mistakes and retrospectively deriving insights from them, you consolidate your wealth of experience and knowledge. In this course, participants will use a case study to cover all aspects of data analysis and the development of machine learning models. Starting with data collection, processing and cleansing the data, through to simple statistical models, hypotheses and finally the training and validation of machine learning algorithms, the course covers all aspects of the work of a data scientist.

• Review
  • Working interactively with Jupyter notebooks
  • Python (basics & libraries: numpy, pandas, matplotlib, scikit-learn)
  • Machine Learning
• Presentation of the case study
  • Description of the problem
  • Joint brainstorming on the approach
  • Presentation of a minimal prototype
• Group work of the participants
  • Regular Q&A rounds
  • Individual assistance
• Presentation of the results

The detailed digital documentation package, consisting of an e-book and PDF, is included in the price of the course.

The course is aimed at users who want to practise working with real data and implementing machine learning solutions using a case study.

Fundamental knowledge of the Python programming language is required. This can be acquired in the courses Python for beginners - introduction to programming or Python for programmers - basics for a quick transition, for example.

Participants should also have gained initial experience with both data analysis and machine learning. This can be acquired, for example, by attending the training courses Python for Data Science - Data Analysis and Insight Machine Learning and Machine Learning - Data Science and Artificial Intelligence.