Wednesday 4/30/2014 We'll be reviewing some basics of probability, developing ways to work with text data, and using a classification algorithm to classify text.
- Articulate Naive Bayes' advantages, flaws, applications and theoretical foundation
- Explain how Naive Bayes is applied to classify text or Spam
- Be familiar with using the N.B. classifiers in NLTK and SKLearn
- Create a basic Naive Bayes classifier
- NB_Gender_Names_NLTK: Notebook covering basics of Naive Bayes with single features
- NB_Biebama_NLTK: Demo: Classifying text as Obama or Bieber
- NB_Movies_SKLearn: Illustration of SK Learn NB functions
- NB_Movies_NTLK: Illustration of NB on text with NLTK
- Add a feature to the NLTK gender classifier to try and improve performance
- Create a classifier to tell the difference between two authors
- Brainstorm classification topics for projects (due May 14)