Classical Text Representation and Search

Why this module belongs in the course

Sparse terms, lexical ranking and dense embeddings capture different signals. Strong search systems compare them instead of assuming that vector similarity solves every query.

Before you begin

The concepts and project evidence from Module 10. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Bag of Words and TF-IDF from First Principles65 min · Beginner
2. Lesson 2Word and Character N-Grams for Text Features60 min · Intermediate
3. Lesson 3Cosine Similarity and Document Ranking65 min · Intermediate
4. Lesson 4Project: Build an Explainable TF-IDF Search Engine100 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• https://scikit-learn.org/stable/modules/feature_extraction.html
• https://scikit-learn.org/stable/common_pitfalls.html
• https://scikit-learn.org/stable/modules/model_evaluation.html
• https://www.postgresql.org/docs/current/textsearch-controls.html