In this chapter, we acquired the fundamental and important concepts of machine learning classification, including types of classification, classification performance evaluation, cross-validation and model tuning, as well as a simple yet power classifier, naive Bayes. We went through the mechanics and implementations of naive Bayes in-depth with a couple of examples and a spam email detection project.
Practice makes perfect. Another great project to deepen your understanding could be sentiment (positive/negative) classification for movie review data (downloaded via http://www.cs.cornell.edu/people/pabo/movie-review-data/review_polarity.tar.gz from the page http://www.cs.cornell.edu/people/pabo/movie-review-data/).
