This module introduces an important concept in machine learning, the selection of the actual features that will be used by a machine learning algorithm. Along with data cleaning, this step in the data analytics process is extremely important, yet it is often overlooked as a method for improving the overall performance of an analysis. This module beings with a discussion of ethics in machine learning, in large part because the selection of features can have (sometimes) non-obvious impacts on the final performance of an algorithm. This can be important when machine learning is applied to data in a regulated industry or when the improper application of an algorithm might lead to discrimination. The rest of this module introduces different techniques for either selecting the best features in a data set, or the construction of new features from the existing set of features.