In this module, you will discover the fundamental concepts that make modern AI search possible. You will learn what a vector database is, how it uses embeddings to understand unstructured data, and why this enables a "semantic search" that goes far beyond simple keywords.

Embed Everything is an intermediate course for ML practitioners and Python developers. You’ll convert unstructured data into numerical embeddings, build a scalable pipeline, apply pre‑trained models to text and images, evaluate with t‑SNE and nearest‑neighbor analysis, and script production‑ready batch processing.

Measure Vector Similarity is an intermediate course for ML engineers and data scientists to master cosine, dot‑product, and Euclidean metrics in retrieval, recommendation, and classification. You’ll implement each with Python/NumPy, explore Amazon and healthcare examples, and complete an assignment notebook benchmarking performance for a portfolio‑ready project.

Master ANN Search is an intermediate course for ML engineers and AI practitioners building high‑speed, large‑scale vector search. You’ll implement FAISS/Annoy, evaluate recall‑vs‑latency trade‑offs, benchmark against brute‑force, and complete a project optimizing a 100 k‑vector index for RAG or recommendation systems.

Tune HNSW is an intermediate course for ML practitioners and AI engineers to master vector‑search optimization. You’ll learn HNSW theory, tune efConstruction, M, and efSearch, build an index from scratch, chart precision‑latency trade‑offs, and complete a portfolio‑ready project optimizing search for chatbots or visual retrieval.

This module explores how generative AI tools can augment your embedding and indexing workflows, from generating boilerplate code to debugging configuration issues. You'll learn effective prompt engineering techniques for ML tasks while understanding when human expertise remains essential.