Gain expertise in using vector databases and improve your data retrieval skills in this hands-on course! During the course, you’ll explore the fundamental principles of similarity search and vector databases, learn how they differ from traditional databases, and discover their importance in recommendation systems and Retrieval-Augmented Generation (RAG) applications. You’ll also dive into key concepts such as vector operations and database architecture to develop a strong grasp of Chroma DB's functionality. You’ll gain practical experience using Chroma DB, a leading vector database solution. And through interactive labs, you’ll learn to create collections, manage embeddings, and perform similarity searches with real-world datasets. You’ll then apply what you’ve learned by creating a real-world recommendation system powered by Chroma DB and an embedding model from Hugging Face; an ideal project to demonstrate your understanding of how vector databases improve search and retrieval in AI-driven applications. If you’re keen to gain expertise in using vector databases and similarity searches, both essential components of the RAG pipeline, then enroll today!
What you'll learn
Understand the fundamental principles of similarity search and vector databases
Differentiate between vector databases and traditional databases
Apply vector operations within Chroma DB
Manage embeddings and create collections in a vector database
Develop a real-world recommendation system using Chroma DB and Hugging Face