Vector DB Foundations, Embeddings & Search Algorithms

Vector DB Foundations: Embeddings & Search Algorithms takes you beyond simple keyword retrieval and into the world of semantic search. Across eight intermediate‑level courses you’ll learn to convert unstructured text and images into meaningful vector embeddings; evaluate them using t‑SNE and nearest‑neighbor analysis; and batch‑process large datasets using production‑style Python scripts. You’ll then master the Hierarchical Navigable Small World (HNSW) algorithm, learning how to manipulate efConstruction, M and efSearch parameters to balance recall and latency for specific use cases. Other courses teach you to compute cosine similarity, dot products and Euclidean distances and to benchmark their impact on ranking and recommendation systems; build and evaluate Approximate Nearest Neighbor (ANN) indices with FAISS and Annoy; explain how vector databases differ from traditional relational or NoSQL systems and build decision frameworks for choosing the right database; design hybrid search combining keyword and vector methods with weighting and NDCG metrics; implement retrieval‑augmented generation pipelines that ground LLMs with external data; and configure multimodal search using Weaviate to search across images and text. Through expert‑led videos, readings, and hands‑on projects you’ll develop portfolio‑ready skills to design, tune and evaluate state‑of‑the‑art vector search systems

• Build and evaluate embedding pipelines for text and images
• Tune HNSW and ANN search parameters
• Explain vector databases and retrieval-augmented generation architectures
• Develop multimodal search applications

• Basic familiarity with concepts and terminology
• Willingness to engage in applied exercises or case studies