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Vector Databases Deep Dive · LearnSpace
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courseraIT и технологии

Vector Databases Deep Dive

Курс от Packt
Средний≈ 6.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers an in-depth exploration of vector databases, focusing on their principles, applications, and future trends. By the end of the course, you'll gain a deep understanding of how vector databases function and how they differ from traditional databases. You'll also grasp the essential concepts that underpin modern data systems, like vectors, embeddings, and distance metrics, and how they enable enhanced search and data retrieval processes. You’ll start by learning the fundamentals of vector databases, including the core concepts and the growing importance of these systems in data management. The course will then walk you through key principles, illustrating how vector databases have emerged as a powerful tool for managing high-dimensional data. As you progress, you will delve into critical topics such as embeddings, distance metrics, and various database indexing techniques, gaining a comprehensive view of how they drive faster, more efficient searches. The course also includes detailed discussions on vector search and similarity, with specific attention to the K-Nearest Neighbors (KNN) and Approximate Nearest Neighbors (ANN) algorithms. You'll learn how these technologies optimize the retrieval of similar data points and understand the trade-offs between different search approaches. Real-world applications, like fraud detection, will be used to demonstrate how these concepts play out in practice. This course is ideal for data professionals, engineers, and developers interested in mastering vector databases. It’s suitable for learners with a foundational understanding of databases and data structures. As the course progresses, you’ll develop expertise in various vector database technologies, from Pinecone and Qdrant to Milvus and Weaviate, with hands-on demos to solidify your skills.

Навыки, которые вы освоите

Artificial Intelligence and Machine Learning (AI/ML)Database TheoryDatabase SystemsDatabase Management SystemsAlgorithmsDatabasesDimensionality ReductionData Storage Technologies

Программа курса

8 модулей · 57 учебных материалов

01Introduction2 материалов

Introduction

Introduction to the CourseВидеоCourse StructureВидео
02Introduction to Vector Databases7 материалов

Introduction to Vector Databases

Introduction to Vector DatabasesВидеоKey Principles of Vector DatabasesВидео

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Packt - Course Instructors

Преподаватель курса

Vector Databases Deep Dive
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 6.5 ч

8 модулей

Язык: Английский

Часть программы вашего университета
Why are Vector Databases All the RageВидео
How Vector Databases Differ from Traditional DatabasesВидео
Advantages & Challenges of Vector DatabasesВидео
Introduction to Vector DatabasesDIALOGUE
Introduction to Vector Databases - AssessmentЗадание
03Vector Database Core Concepts14 материалов

Vector Database Core Concepts

Introduction to VectorsВидеоReal World Illustration on VectorsВидеоVectors and Their Roles in DatabasesВидеоIntroduction to EmbeddingsВидеоEmbeddings Illustrations - Fraud Detection ExampleВидеоIntroduction to Dimensionality and High-Dimension SpacesВидеоChallenges with High-Dimensional DataВидеоDistance Metrics and SimilarityВидеоEuclidean DistanceВидеоManhattan DistanceВидеоCosine DistanceВидеоJaccard SimilarityВидеоUnderstanding Vector DatabasesDIALOGUEVector Database Core Concepts - AssessmentЗадание
04Understanding Search Similarity6 материалов

Understanding Search Similarity

The Importance of Search SimilarityВидеоK-Nearest NeighborsВидеоApproximate Nearest NeighborsВидеоKNN vs. ANNВидеоUnderstanding Similarity Search and KNN vs ANNDIALOGUEUnderstanding Search Similarity - AssessmentЗадание
05Indexing and Querying14 материалов

Indexing and Querying

Indexing StrategiesВидеоFlat IndexВидеоFlat Index Imagined - Real World IllustrationВидеоInverted File IndexВидеоInverted File Index Imagined - Real World IllustrationВидеоApproximate Nearest Neighbors Oh Yeah - ANNOYВидеоANNOY Imagined - Real World IllustrationВидеоProduct QuantizationВидеоProduct Quantization Imagined - Real World IllustrationВидеоHierarchical Navigable Small World (HNSW)ВидеоHNSW Imagined - Real World IllustrationВидеоSelecting the Right IndexВидеоIndexing and Querying in Vector Databases with HNSWDIALOGUEIndexing and Querying - AssessmentЗадание
06Working with Vector Databases7 материалов

Working with Vector Databases

Vector Database or Vector StoreВидеоPineconeВидеоQdrantВидеоMilvusВидеоWeaviateВидеоChoosing Between Vector Databases and Vector StoresDIALOGUEWorking with Vector Databases - AssessmentЗадание
07Demos4 материалов

Demos

Pinecone DemoВидеоWeaviate DemoВидеоInteracting with a Vector Database using PineconeDIALOGUEDemos - AssessmentЗадание
08The Future of Vector Databases3 материалов

The Future of Vector Databases

The Future of Vector DatabasesВидеоFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание