Graphlet-AI/graphml-class
Full Stack Graph Machine Learning: Theory, Practice, Tools and Techniques
This is a course designed for data scientists and machine learning engineers who want to build and analyze knowledge graphs and network motifs. It provides a structured learning environment to transform raw data, like XML archives, into structured knowledge graphs. The output is a deeper understanding of graph machine learning techniques and practical experience with tools like PySpark and GraphFrames.
Use this if you are a data scientist or machine learning engineer looking to learn and apply full-stack graph machine learning, including knowledge graph construction and network motif analysis.
Not ideal if you are looking for a pre-built application or library that solves a specific business problem without needing to understand the underlying graph machine learning techniques.
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80
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 06, 2025
Commits (30d)
0
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