Vector Databases for Machine Learning and RAG Systems

As generative AI and large language models reshape software development, understanding how to store and retrieve unstructured data efficiently is an essential skill. Vector databases form the backbone of modern semantic search and context-aware artificial intelligence. This text-only course guides you from the fundamental concepts of vector embeddings to implementing production-ready vector stores. You will learn how to represent data as vectors, select the right database architecture, and build robust Retrieval-Augmented Generation (RAG) systems that ground AI responses in real-world facts. What you'll learn: - Understand the core concepts of vector embeddings and high-dimensional space. - Compare popular vector database solutions and indexing algorithms like HNSW and IVF. - Perform similarity searches using cosine distance, dot product, and Euclidean metrics. - Build and configure a Retrieval-Augmented Generation pipeline to ground AI responses. - Implement metadata filtering and hybrid search techniques to improve retrieval accuracy. - Manage database scaling, indexing trade-offs, and production deployment strategies. The course begins with foundational definitions of vector space before moving into step-by-step written explanations of database setup, querying, and system integration. You will practice through clear code snippets and conceptual exercises designed to solidify your engineering skills. This course is designed for software developers, data practitioners, and AI enthusiasts who want to build a solid foundation in vector search. No prior experience with vector databases is required, though basic familiarity with programming concepts is helpful. Start reading today to unlock the power of semantic search in your machine learning projects.