  Despite the tremendous efforts to democratize machine learning, especially in
applied-science, the application is still often hampered by the lack of coding
skills. As we consider programmatic understanding key to building effective and
efficient machine learning solutions, we argue for a novel educational approach
that builds upon the accessibility and acceptance of graphical user interfaces
to convey programming skills to an applied-science target group. We outline a
proof-of-concept, open-source web application, the PHOTON Wizard, which
dynamically translates GUI interactions into valid source code for the Python
machine learning framework PHOTON. Thereby, users possessing theoretical
machine learning knowledge gain key insights into the model development
workflow as well as an intuitive understanding of custom implementations.
Specifically, the PHOTON Wizard integrates the concept of Educational Machine
Learning Code Generators to teach users how to write code for designing,
training, optimizing and evaluating custom machine learning pipelines.
