Artificial intelligence relies on vectors and embeddings to make sense of information in ways traditional databases cannot. This course begins by exploring the foundations, starting with the concept of vectors, embeddings, and similarity metrics. You’ll see how raw data is transformed into meaningful numerical representations that AI can analyze and compare. From there, you move into vector databases, the specialized systems designed to manage structured and unstructured data at scale. You’ll learn the workflows of vector search, understand how these databases function, and explore how to choose the right one for your projects. The course provides context for both the theory and the practical steps of working with vectorized information. The final section focuses on industry use cases that highlight the transformative impact of vector databases. You’ll discover how they power semantic search, drive recommendation engines, enhance retrieval-augmented generation, and support anomaly detection and visual search. By the end of the course, you’ll be prepared to apply these concepts to build innovative AI-driven solutions.