Learn how to leverage Qdrant, a powerful vector database and similarity search engine, in this 24-minute tutorial that demonstrates the implementation of semantic search and recommendation systems. Generate meaningful search results and recommendations by working with sample datasets, turning embeddings and neural network encoders into practical applications. Master the fundamentals of vector similarity search while exploring matching, searching, and recommendation functionalities through hands-on exercises.