The didactic concept centers on teaching machine learning through an action-oriented, constructivist, and problem-based approach. Students actively gain knowledge by working on real-world challenges, supported by micro-lectures and hands-on tutorials. The course is structured in two phases: foundational learning and practical application in collaborative projects. Overall, it emphasizes data literacy, practical ML skills, interdisciplinary teamwork, and critical awareness of ML limitations. The didatic concept is explained further in the white paper and the conference paper.