If I'm reading this correctly, it's just wrong. Whatever the distinction between data analysis and ML might be, it is more than just whether your data and predi

If I'm reading this correctly, it's just wrong. Whatever the distinction between data analysis and ML might be, it is more than just whether your data and predicted quantities are discrete or continuous. > Categorical Independent vs. Categorical Dependent, for example, is fairly restrictive, as makes logical sense. You may cross-tabulate, you may score likelihood based on previous observation, but obviously, because all of the data involved are non-numeric, there's no chance for regression, ANOVA, etc If you are implying that categorical -> categorical predictions are not ML: as a counter example, natural language is a categorical (words) input that could be used to predict any number of categorical variables (parse trees, semantic categories, etc). I think it's safe to say that the field of NLP is doing machine learning.