The main objective of this chapter is to show how to provide explanations for intrinsic explainable models. As we said, for this category of ML models, XAI can be achieved by looking at the internals with the proper interpretations of the weights and parameters that build the model. We will make practical examples (using Python code) that will deal with the quality of wine, survival properties in a Titanic-like disaster, and for the ML-addicted the ever-green categorization of Iris flowers.