Abstract
Effectively applying machine learning methods, particularly in applied sciences, can pose significant challenges. However, when employed correctly, these algorithms prove to be powerful tools, offering substantial benefits across a wide range of research applications. Fine-tuning them to individual needs and circumstances requires making a number of relevant and wellinformed choices, all of which can profoundly impact the quality of the outcome. In this thesis, I present a comprehensive overview over the machine learning process, along with two use-cases of successful machine learning application in practice.
