Machine learning meets and shapes all of our daily lives in many situations: while shopping online, when a streaming provider suggests new music or films, when a voice assistant turns on the light, or indirectly through the possibilities that machine learning opens up in science and technology.
Machine learning can be applied in any programming language. But one language in particular has become established for this task in science and industry: Python. Thanks to its many open extensions, Python offers the perfect foundation for processing data, training a model and applying it to new data.
Topics
- What actually is machine learning?
- Application examples
- Data collection and preparation
- What sets supervised and unsupervised learning apart?
- Overview of models and algorithms
- Training models on real data
- Critical evaluation and comparison of models
- Ethical implications and future prospects
Requirement: Some initial experience with Python, for example from our introductory Python course.