Now that you know how to binarize data for different purposes, we can look into other types of data, such as categorical or multi-labeled data, and how to make them numeric. Most advanced deep learning algorithms, in fact, only accept numerical data. This is merely a design issue that can easily be solved later on, and it is not a big deal because you will learn there are easy ways to take categorical data and convert it to a meaningful numerical representation.
Categorical data has information embedded as distinct categories. These categories can be represented as numbers or as strings. For example, a dataset that has a column named country with items such as "India", "Mexico", "France", and "U.S". Or, a dataset with zip codes such as 12601, 85621, and 73315. The former is non-numeric categorical data, and the latter is numeric categorical data. Country names would need to be converted to a number to be usable...
