This hands-on course provides a practical introduction to vector databases, one of the core technologies behind modern AI applications. If you’ve ever wondered how AI systems find documents based on meaning instead of keywords, power semantic search, or retrieve relevant information for large language models, this course is for you.
We focus on learning by doing. There are no theory-heavy slides or unnecessary complexity. Instead, you’ll work directly with vector databases and build practical examples from the very beginning. You’ll start by understanding what embeddings are, how they represent the semantic meaning of text, and why traditional relational and NoSQL databases are not designed for similarity search.
Then we’ll move straight into practice. You’ll learn how to create embeddings, store them inside vector databases, and perform fast semantic searches that return results based on meaning rather than exact text matches.
