There are different approaches to create and train ML models. In this section, we show what these approaches are and how they differ. Apart from the approach we use to create a ML model, there are also parameters that manage how this model behaves in the training and evaluation processes. Model parameters can be divided into two distinct groups, which should be configured in different ways. The last crucial part of the ML process is a technique that we use to train a model. Usually, the training technique uses some numerical optimization algorithm that finds the minimal value of a target function. In ML, the target function is usually called a loss function and is used for penalizing the training algorithm when it makes errors. We discuss these concepts more precisely in the following sections.