unsupervised learning, embeddings, and recommenders

Unsupervised workflows help when labels are missing but grouping, search, dedupe, or recommendation would make messy information easier to use. This chapter teaches the plain-English idea: similar things can be placed near each other, but nearby does not mean true.

The examples use customer feedback themes, policy-doc search, research-note dedupe, and related help articles. You will practice choosing useful metadata, inspecting rankings, spotting bad groups, and adding human review where similarity can mislead.

By the end, you can turn embeddings or clusters into a reviewable artifact: query, metadata, examples, suggested group or match, and the human check needed before anyone acts on it.