The Naive Bayes series of algorithms are some of the simplest classification algorithms, but they tend to offer reasonably good results very quickly for a number of problems, including Natural Language Processing problems such as spam classification, as well as more classical feature-driven classification. In this talk, we will look at the math behind Naive Bayes classification, solving problems by hand before looking at a package in R which solves the problem for us. By the end of this talk, you should be able to apply Naive Bayes to existing problems. No experience with statistics is required, although there will be a small amount of math.
No recordings or additional media are available for this talk.