This course explains embeddings in plain English and shows where they are used in real products.
Includes practical lessons, exercises, and a clear next step after you enroll.
About this course
Curriculum
Lessons are listed in order. Each one has a goal, a walkthrough, practice, and a short check before you continue.
- 01What an embedding is in one pictureA list of numbers that puts similar meanings nearby.
- 02Similarity, nearest neighbors, and rankingA question vector versus document vectors.
- 03Where embeddings show up in productsSearch, clustering, recommendations, RAG.
- 04Chunking and metadata — the practical bitsWhat you embed, and what you store beside it.
- 05Limits: antonyms, recency, and multilingualNearby is not opposite-aware or automatically up to date.
- 06A tiny mental demo you could build laterTen sentences, one query, show top three.
- 07Workshop: a complete pass on a real taskCombine the earlier lessons into one working example you can keep.
- 08Quality bar, common mistakes, and a 30-day planLock in a checklist so the skill survives after the course ends.