Algorithms form the basis of many software in the field of machine learning. How these algorithms work is usually hidden from the users of the software. This leads to the paradoxical situation that it is often unclear why a certain machine learning procedure works well or why it does not work well. It is often even unclear how the results are produced, resulting in data distortion and undesirable results with high negative costs. In this workshop we want to look under the hood of well-known machine learning methods such as linear regression, reinforcement learning, k-means clustering, decision trees and other methods and explain them using practical examples. At the end of the workshop, the participants should be able to decide for themselves which processes are best suited for their applications and how they can optimally tailor these processes to their needs.