Graph ML: Build Knowledge Graphs using Generative AI and LLMs
Code: https://github.com/neo4j-partners/neo...
Learn how to build powerful knowledge graphs from raw resumes using LLMs, Neo4j, and LangChain. In this step-by-step tutorial, we’ll extract entities, create relationships, and query graph databases with both Cypher and natural language. Perfect for anyone exploring Graph ML, generative AI, and recruiting automation.
🔥 Extract entities & relationships from resumes using LLMs
⚡ Ingest structured data into Neo4j with Cypher queries
💡 Build interactive graph-based applications with LangChain & Gradio
📈 Generate node embeddings & graph features for ML use cases
✨ Create a chatbot that converts natural language into Cypher queries
knowledge graphs with LLMs
Neo4j knowledge graph tutorial
resume parsing with AI
entity extraction generative AI
Cypher query language tutorial
build chatbot with LangChain
knowledge graph machine learning
graph databases for recruiters
AI for HR automation
Google Vertex AI Neo4j tutorial
Connect with me:
💻 GitHub: https://github.com/KannamSridharKumar
💼 LinkedIn: / sridharkumarkannam
▶️ YouTube: / @sridharkumarkannam
🎓 Google Scholar: https://scholar.google.com/citations?...
🐦 Twitter/X: https://x.com/SridharKKannam
✍️ Medium: / kannamsridharkumar
📘 Learn Data Science, ML, AI and Automation through step-by-step hands-on tutorials.
✉️ Pls do not hesitate to contact me at [email protected] if you have any questions or feedback.
🙏 If you found my content helpful, pls support & share in your network. Your support is greatly appreciated.
✨ Thank You & Happy Learning :)
👨 Sridhar Kumar Kannam
✉️ [email protected]
#datascience #machinelearning #deeplearning #datanalytics #predictiveanalytics
#artificialintelligence #generativeai #largelanguagemodels #naturallanguageprocessing
#computervision #transformers #embedding #graphml #graphdatascience
#datavisualization #businessintelligence #montecarlosimulation #simulation #optimization
#python #aws #azure #gcp