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.

Intermediate

19 Lessons

3h 15min

Certificate of Completion

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.

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This course includes

17 Playgrounds

This course includes

17 Playgrounds

Course Overview

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...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

What You'll Learn

An understanding of vector databases and their significance in modern-world AI applications

Course Content

Before Getting Started

Get familiar with vector databases, embeddings, and their applications in AI systems.

Introduction to the Course

Getting Started with Vector Databases and Embeddings

Look at the essentials of vector databases, embeddings, similarity measures, and multimodal integration.

Introduction to Vector Databases and Embeddings Methods for Measuring Similarity between Embeddings Embedding Models for Different Data Types Generating Text Data Embeddings with BERT Generating Image, Video, and Audio Embeddings Multimodal Embeddings Embeddings vs. Fine-Tuning Quiz: Getting Started with Vector Databases and Embeddings

Working with Vector Databases

Work your way through leveraging open-source vector databases, hands-on practices, and optimization techniques.

Overview of Open-Source Vector Databases Practice Using ChromaDB for Multimodal Embeddings HNSW Indexing in Vector Databases for Performance Optimization

Applications of Vector Databases Quiz: Working with Vector Databases

Developing a Music Recommendation System

Apply your skills to develop and optimize a music recommendation system using vector databases.

Introduction to the Music Recommendation System Generating Embeddings for Audio Files and Metadata of Songs Storing Audio and Metadata Embeddings to Vector Database Querying the Database to Generate Recommendations

Wrapping Up

Take a closer look at vector databases, embedding techniques, and their practical applications.

Conclusion

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