Summary

First, you were introduced to the different types of features that you might have to work with. Identifying the type of variable you'll be working with is very important for defining the types of transformations and techniques that can be applied to each case.

Then, we learned how to deal with categorical features. We saw that, sometimes, categorical variables do have an order (such as the ordinal ones), while other times, they don't (such as the nominal ones). You learned that one-hot encoding (or dummy variables) is probably the most common type of transformation for nominal features; however, depending on the number of unique categories, after applying one-hot encoding, your data might suffer from sparsity issues. Regarding ordinal features, you shouldn't create dummy variables on top of them, since you would be losing the information of order that's been incorporated into the variable. In those cases, ordinal encoding is the most appropriate transformation...