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Build Chroma Search

Курс от Coursera
Средний≈ 2.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Build Chroma Search is an intermediate, project-based course for developers and aspiring machine learning engineers who want to build and deploy a complete, real-world semantic search application. In today's AI-driven landscape, keyword search is no longer enough; this course teaches you how to leverage the power of vector embeddings and the specialized vector database, Chroma, to create a search engine that understands meaning, not just words. You will progress through a full development lifecycle, from indexing a document collection to exposing your search functionality through a deployable Flask API. The course places a strong emphasis on professional standards, guiding you to quantitatively measure your API's performance using critical relevance metrics like Mean Reciprocal Rank (MRR) and precision@5. Through hands-on labs and a final summative project, you will not only build a functional search API but also produce an evaluation report to validate its quality, equipping you with a portfolio-ready project and the skills to tackle advanced information retrieval tasks.

Навыки, которые вы освоите

Semantic WebFlask (Web Framework)Python ProgrammingAPI TestingApplication Programming Interface (API)Quality AssuranceModel DeploymentVector DatabasesEmbeddings

Программа курса

3 модулей · 18 учебных материалов

01Build the Semantic Search Engine8 материалов
The Problem with Keyword SearchDIALOGUEFrom Keywords to Understanding: The Power of Semantic SearchВидеоThe Core Concepts: Embeddings and Vector DatabasesЧтениеChroma: The Vector Database for Semantic SearchВидео

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Professionals in the Industry

Преподаватель курса

Build Chroma Search
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Обучение на Coursera

≈ 2.8 ч

3 модулей

Язык: Английский

Часть программы вашего университета
Indexing Documents with ChromaВидео
[Ungraded Lab] Hands On Learning: Build and Query a Chroma CollectionЧтение
Hands-On Learning: Build and Query a Chroma CollectionЛабораторная
Knowledge Check: Embedding Model Evaluation and BenchmarkingЗадание
02Build the Evaluation Pipeline6 материалов
Objective Metrics: From Opinion to Production-ReadyВидеоHow to Measure Relevance: MRR & Precision@5 ExplainedЧтениеEvaluating Semantic Search with MRR and Precision@5Видео[Ungraded Lab] Hands On Learning: Implement Your Evaluation ScriptЧтениеHands-On Learning: Implement Your Evaluation ScriptЛабораторнаяHands-On Learning: Calculating Relevance MetricsЗадание
03Deploy & Analyze the Search API4 материалов
From Local Script to Global Service: Powering Search with APIsВидеоBuilding a Flask API for Your Search EngineВидеоScenario: "My Search Precision is Low! What Now?"DIALOGUEBuild, Deploy, and Evaluate Your Search APIЗадание