Master vector databases for AI applications. Learn embeddings, similarity search, and hands-on setup of Pinecone, pgvector, and Chroma. Understand indexing strategies, hybrid search, performance optimization, and how to choose the right database for your use case.
Vector databases have become a core building block of modern AI applications, powering everything from semantic search to retrieval-augmented generation. This free intermediate course walks you through exactly how they work: starting with the concept of embeddings (how text and other data get converted into numbers that capture meaning), then moving into similarity search, indexing strategies, and the practical differences between leading options like Pinecone, pgvector, and Chroma.
The course covers the full decision-making process that developers and AI practitioners face in the real world. You will learn how to set up each of the three databases hands-on, understand querying and filtering, work with metadata and hybrid search, and apply performance optimization and scaling techniques. A dedicated module on cost comparison and another on choosing the right database for your use case make it straightforward to apply what you learn to your own projects.
Whether you are a developer building your first AI-powered application, a data professional expanding into machine learning infrastructure, or a student studying AI, this course gives you a solid, practical foundation. The final module covers integration with LangChain and AI SDK so you can connect vector databases to the AI tools you are already using. The course is completely free, and completing it along with the final exam earns you a certificate of completion you can share on LinkedIn or add to your resume.
17 modules • 17 lessons
Finish every lesson and pass the final exam to earn this free, shareable certificate.
Verify
June 15, 2026
This certifies that
has successfully completed the course
Sample preview. Your name appears on the certificate when you complete the course. Learn more
The course covers the full vector database stack: embeddings, similarity search, indexing strategies, querying and filtering, hybrid search, performance optimization, scaling, and cost comparison. You also get hands-on setup lessons for three specific databases, Pinecone, pgvector, and Chroma, plus a final module on integrating them with LangChain and AI SDK.
Yes, the course is completely free with no account required to start. If you complete all the lessons and pass the final exam, you earn a certificate of completion at no cost.
This is an intermediate course, so some prior familiarity with basic programming concepts and a general understanding of AI or machine learning will help you get the most from it. You do not need prior experience with databases or vector search specifically.
The course includes dedicated hands-on setup modules for Pinecone (cloud-hosted), pgvector (the PostgreSQL extension), and Chroma (local and free). A separate module surveys the broader vector database landscape so you understand the trade-offs beyond just these three.
Yes. Completing the course and passing the final exam earns you a certificate of completion that you can add to your LinkedIn profile or resume to show practical knowledge of vector databases and AI application infrastructure.
Build a private, offline knowledge base over your own notes and PDFs using a local AI model. A hands-on beginner micro course: run a model with Ollama, turn documents into embeddings, store them in a local Chroma database, and ask questions answered from your own files. No cloud APIs, no fees, your data stays on your machine. Want to deploy it as a web app instead? See the Full-Stack RAG course (/courses/fullstack-rag-nextjs-supabase-gemini).
Master linear algebra through the lens of artificial intelligence. Learn vectors, matrices, dot products, eigenvalues, and tensors by seeing exactly how they power neural networks, transformers, embeddings, and other AI systems.
Master the art of chaining AI prompts and building sophisticated workflows. Learn to design multi-step AI pipelines, handle errors gracefully, implement branching logic, manage context, and build production-ready AI workflows for research, content creation, and code generation.
Master database internals, indexing, and schema design for modern AI applications. Learn how SQL databases power production AI systems at TikTok, Uber, and Netflix. Build RAG systems, feature stores, and high-performance pipelines with PostgreSQL and pgvector.
Create and edit videos using AI tools. Master text-to-video generation with Runway and Pika, AI editing with CapCut and Descript, avatar videos with HeyGen and Synthesia, and complete video production workflows.
Learn to leverage Claude AI for effective code review in 30 minutes. Master prompts for finding bugs, security vulnerabilities, and refactoring suggestions with hands-on practice.
