Build, query, and integrate knowledge graphs to power real-world AI applications — hands-on with Neo4j, Cypher, and LLMs
What you'll learn
⚡ Understand the fundamentals of graph theory and knowledge graph design
⚡ Model complex enterprise domains as knowledge graphs
⚡ Build and query knowledge graphs using Neo4j and Cypher
⚡ Design and apply ontologies and schemas for real-world use cases
⚡ Integrate knowledge graphs with Large Language Models (LLMs) for AI agents and RAG pipelines
⚡ Apply knowledge graphs to pharma, finance, and enterprise use cases
⚡ Deploy and maintain a production-ready knowledge graph solution
Requirements
❗ Basic Python
❗ Familiarity with databases
❗ General awareness of AI/ML concepts
Description
This course contains the use of artificial intelligence to narrate the audios. The reason for this decision - AI narration voice used is better quality than even human voice and the speed of production was much faster! However, the content of the course has been compiled and 100% reviewed by Bashir Ahmed, the creator of this course.
Knowledge Graphs for Enterprise AI: A Practical Guide
Unlock the power behind the world's most intelligent AI systems. Knowledge graphs are the secret infrastructure driving enterprise search, recommendation engines, and next-generation RAG pipelines — and now you can build them too.
In this hands-on course, you'll go from zero to production-ready, learning how to model complex real-world data as connected knowledge, query it with SPARQL and Cypher, and wire it into LLM-powered applications that actually understand context.
Whether you're an AI engineer looking to supercharge your retrieval pipelines, a data architect designing smarter systems, or a developer curious about how companies like Google, Amazon, and Microsoft structure knowledge at scale — this course gives you practical skills you can apply on Monday morning.
What you'll walk away with
✨ A working knowledge graph built from real enterprise data
✨ LLM integrations that use graph context for richer, more accurate answers
✨ Confidence to design and deploy KG-backed AI solutions at work
No PhD required. Just curiosity and a willingness to think in connections. Relational databases no longer enough to power up enterprise AI Agentic solutions, you need knowledge graphs! If you are a solution architect or data engineer or LLM developer, this course is a must, and it can supercharge your career!
Who this course is for
⭐ Data engineers, AI architects, enterprise tech leads, developers building AI-powered applications
Homepage

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https://www.udemy.com/course/knowledge-graphs-for-enterprise-ai-a-practical-guide



https://rapidgator.net/file/4d9c90d1f571fc1f575e4f09bcaf8e41/Knowledge_Graphs_for_Enterprise_AI_A_Practical_Guide.part3.rar.html
https://rapidgator.net/file/32b02d1c9abe87f9bb4e7a1ea2ca4482/Knowledge_Graphs_for_Enterprise_AI_A_Practical_Guide.part2.rar.html
https://rapidgator.net/file/4107cde6184e6731b70e3dd76f918c53/Knowledge_Graphs_for_Enterprise_AI_A_Practical_Guide.part1.rar.html