Vector Databases: From Embeddings to Applications

This course will teach you how to generate embeddings and use vector databases for semantic search apps, recommendations, and multimodal solutions.

Intermediate

18 Lessons

3h 15min

Certificate of Completion

This course will teach you how to generate embeddings and use vector databases for semantic search apps, recommendations, and multimodal solutions.

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This course includes

17 Playgrounds

This course includes

17 Playgrounds

Course Overview

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, d...Show More

What You'll Learn

An understanding of vector databases and their significance in modern data applications

Hands-on experience generating embeddings for different data types

The ability to find similar embeddings within a vector space

The ability to build efficient, practical applications using the power of vector databases and embeddings

Hands-on experience using open-source vector databases and optimizing their performance

What You'll Learn

An understanding of vector databases and their significance in modern data applications

Course Content

Before Getting Started

This chapter introduces the concept of vector databases and embeddings and provides an overview of this course.

Introduction to the Course

Getting Started with Vector Databases and Embeddings

This chapter covers vector DB components, their working, generating embeddings for text, image, video, audio, multimodal data, and semantic search.

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

In this chapter, you’ll explore open-source vector databases, their design and strengths, hands-on with ChromaDB, and HNSW indexing for optimized performance.

Overview of Open-Source Vector Databases

Practice Using an Open-Source Vector Database

Performance Optimization Techniques

Quiz: Working with Vector Databases

Developing a Music Recommendation System

In this chapter, you’ll learn to apply the embedding and vector DB concepts to real-world applications by developing a music recommendation system.

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

This chapter concludes our vector databases course, covering embedding generation, storage in vector DBs, and building real-world semantic search applications.

Conclusion

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@_webarchitect_

Evan Dunbar

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