This document introduces Naive Bayes classification. It discusses using Bayes' rule to calculate the probability of an email being spam given the presence of a word. An example is worked out classifying an email as spam or ham based on the word "meeting". The document then expands on this to consider multiple words using a naive Bayes model that treats words as independent predictors. It notes that wrangling data is important and discusses extracting and classifying articles using Naive Bayes as an example.