summary and outlook in this we discussed how to deal with different data types (in particularwith categorical variableswe emphasized the importance of representing data in way that is suitable for the machine learning algorithm--for exampleby one-hotencoding categorical variables we also discussed the importance of engineering new featuresand the possibility of utilizing expert knowledge in creating derived features from your data in particularlinear models might benefit greatly from generating new features via binning and adding polynomials and interactionswhile more complexnonlinear models like random forests and svms might be able to learn more complex tasks without explicitly expanding the feature space in practicethe features that are used (and the match between features and methodis often the most important piece in making machine learning approach work well now that you have good idea of how to represent your data in an appropriate way and which algorithm to use for which taskthe next will focus on evaluating the performance of machine learning models and selecting the right parameter settings representing data and engineering features