4.3
Intermediate
3h 15min
Updated 2 months ago
Vector Databases: From Embeddings to Applications
This course teaches how data vectorization and vector databases enable context-based search over keyword matching, multimodal data search, enhance recommendation systems, and power LLMs.
Vector databases transform how we search, analyze, and recommend data in today’s AI-driven world. These databases are at the heart of modern applications like semantic search, multimodal search, recommendation systems, and retrieval augmented generation (RAG) for large language models. By using embeddings, which are numerical representations that capture the meaning of data, vector databases allow us to find similar information quickly and accurately, even across vast datasets. This makes them essential for building intelligent systems that understand data beyond simple keyword matching.
In this vector databases course, you’ll learn to generate embeddings for various data types and use vector databases to store and query them. Using the power of embeddings and vector databases, you’ll build semantic search apps, recommendation systems, and multimodal search solutions.
After completing this course, you’ll have the skills to determine when and how to effectively apply vector databases to different projects.
Vector databases transform how we search, analyze, and recommend data in today’s AI-driven world. These databases are at the hea...Show More
WHAT YOU'LL LEARN
An understanding of vector databases and their significance in modern-world AI applications
An understanding of embeddings in vector databases
The ability to build efficient, intelligent applications using the power of vector databases and embeddings
Hands-on experience generating unimodal and multimodal embeddings
The ability to find similar embeddings within a vector space
Hands-on experience using the Chroma vector database
Hands-on experience building unimodal and multimodal semantic search applications
Hands-on experience building embeddings and vector database-powered music recommendation system
An understanding of HNSW, the most widely used vector indexing technique used in vector databases for performance optimization
An understanding of vector databases and their significance in modern-world AI applications
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Content
2.
Getting Started with Vector Databases and Embeddings
8 Lessons
Look at the essentials of vector databases, embeddings, similarity measures, and multimodal integration.
Introduction to Vector Databases and EmbeddingsMethods for Measuring Similarity between EmbeddingsEmbedding Models for Different Data TypesGenerating Text Data Embeddings with BERTGenerating Image, Video, and Audio EmbeddingsMultimodal EmbeddingsEmbeddings vs. Fine-TuningQuiz: Getting Started with Vector Databases and Embeddings
3.
Working with Vector Databases
5 Lessons
Work your way through leveraging open-source vector databases, hands-on practices, and optimization techniques.
4.
Developing a Music Recommendation System
4 Lessons
Apply your skills to develop and optimize a music recommendation system using vector databases.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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