ML 6.1Lesson outlineLesson 1 of 3 in Module 6
Turning Text into Numbers
Turn documents into vectors, and see what each representation keeps and loses.
Video in production.
Lesson outline: Turning Text into Numbers
In this lesson11 parts
- 01A model needs a table of numbers
- 02The messages, already split
- 03Tokens
- 04The bag of words
- 05Choices before counting
- 06TF-IDF
- 07Sentence embeddings
- 08Which representation separates spam
- 09Where it misleads
- 10Code
- 11Recap, your challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.
- ML 1.4: Samples, Features and Labels
- 1.5
- 4.1
- 4.4–4.5
- 5.3
- 7.2
- Python 3.5: Strings
- Maths 2.3 (completed 2026-10-17: the comparison of representations uses logistic regression: The Dot Product, Angle and Projection
- cross-validated average precision and the one-standard-error rule)