In this course, you’ll learn to generate embeddings and utilize vector databases to build semantic search apps, enhance recommendation systems, and develop multimodal search solutions.

You’ll begin by understanding the concept of embeddings, vector databases, and their importance in modern world applications. You’ll learn to generate text embeddings with BERT, image and video embeddings with CNNs, audio embeddings with mel spectrogram, and multimodal embeddings with CLIP. You’ll explore the architecture, design choices, and key features of different open-source vector databases, focusing on using Chroma for storage and queries on multimodal data. You’ll learn performance optimization techniques, especially HNSW. You’ll also learn to develop applications, including similarity search applications for images, videos, and audio.

By the end of this course, you will have a solid foundation in vector databases and be able to apply your knowledge to small—and large-scale projects.