The process of building a machine learning model does not only consist of selecting and training a model. It is also about defining the problem, collecting and pre-processing data. On many occasions, it might not be clear whether a change in the data or model will result in an improvement. Therefore, the process of building a machine learning model is often an iterative one. On top of this, ethical aspects such as fairness should not be forgotten, if we don’t want our models to contribute to inequality in our societies. With this section, we hope to have given you some practical tools that will be helpful when you apply machine learning in the future.