Module 32

Text and machine learning

Advanced module (after graduation): bag of words, TF-IDF, Naive Bayes, a sentiment classifier, n-grams, a support-ticket router

When you finish this module you can say:

I can turn text into numbers (bag of words, TF-IDF) and build a fast classifier, compare it with a lazy baseline, and read its mistakes to say where it falls short.

By the end of this module you will be able to build a fast text classifier, compare it with a lazy baseline, and read its mistakes to say when a language model is needed.

Lessons

6 lessons · ≈ 70 min

1. Bag of words11 min
2. TF-IDF11 min
3. Naive Bayes12 min
4. A sentiment classifier12 min
5. n-grams and negation12 min
6. Project: a support-ticket router12 min · project

Practice exercises

Short writing exercises, independent of the lessons: Go to the exercises

1. Word totals
2. The probability of a class
3. List the word pairs