Gain expertise in using vector databases and improve your data retrieval skills in this hands-on course! Start by exploring the fundamental principles of similarity search and vector databases, understanding how they differ from traditional databases, and recognizing their importance in recommendation systems and Retrieval-Augmented Generation (RAG) applications.

Build on this foundation with practical experience using ChromaDB—one of the leading vector database solutions. Through interactive labs, learn to create collections, manage embeddings, and perform similarity searches with real-world datasets. Understand key concepts such as vector operations and database architecture to develop a strong grasp of ChromaDB's functionality. Apply what you’ve learned by creating a real-world recommendation system powered by ChromaDB and an embedding model from Hugging Face. This project will enhance your understanding of how vector databases improve search and retrieval in AI-driven applications. Throughout the course, engage in practical exercises to refine your skills in database management, similarity search techniques, and advanced vector operations. By the end, you’ll have a thorough understanding of vector databases and similarity searches, which are essential components of the RAG pipeline.