Embeddings
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10 lessons3 levels1 capstone project
Understand how text, images, and other data become vectors; learn semantic similarity, normalization, distance metrics, chunking, model choice, and embedding quality for retrieval and recommendation.
BASIC
- What Is a Vector Database? — IBM Technology (9 min)
Introduces embeddings and why semantic similarity requires vector representations. - Transformers, the Tech Behind LLMs — 3Blue1Brown (27 min selected)
Build intuition for learned representations and how transformers encode meaning. - Vector Databases Explained — Data Science Dojo (20 min selected)
Connect embeddings, similarity metrics, indexing, and retrieval behavior.
INTERMEDIATE
- What Is a Vector Database? Beyond Coding - Search Is In Everything You Build — IBM Technology
Apply Embeddings through the adjacent capability: Search & AI Search. - Agents, Prompts & RAG — Stanford CS230
Apply Embeddings through the adjacent capability: Prompt & Context Engineering. - Prompting 101 | Code w/ Claude — Anthropic
Apply Embeddings through the adjacent capability: Prompt & Context Engineering.
ADVANCED
- Building Text Generation Applications — Microsoft Developer
Extend Embeddings into senior/production practice through Generative AI. - Production RAG Agent — Google Cloud
Extend Embeddings into senior/production practice through Vector Databases. - Generative AI for Beginners (Full Series) — Microsoft Developer
Extend Embeddings into senior/production practice through Generative AI.