The Conference for Machine Learning Innovation

Graph-Powered Machine Learning Workshop

Workshop
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos
Friday, December 2 2022
13:30 - 17:00

From graph analytics to graph neural networks: Making the most of your graph data
In this workshop, you will gain hands-on experience with the latest topics in Analytics and Machine Learning: Graph Powered Machine Learning.
Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. More recently, Graph Machine Learning applied directly to graphs using graph algorithms and machine learning has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different way.
We will cover Graph Basics, Graph Analytics, and Graph Machine Learning with many hands-on experiences.

Key Topics

  • Hands-on Experience and best practices on
    • Graph Modeling 
    • Graph Analytics
    • State of the Art Graph Machine Learning 
  • Tradeoffs between different Options in respect to performance and complexity

Target Audience

  • Data Scientist/Analyst 
  • Data Architects
  • Engineers interested in Data Science
  • Scientists interested  in Data Science
  • Architects/Data Solution Specialists 

Goals

Understand the value of Graph Data and its applications and best practices in Analytics and Machine Learning.

Session outline:

  • Introduction to Graph
  • Graph Use Cases 
  • Graph Modeling & Storage
  • Graph Analytics 
  • Graph Machine Learning
    • Embeddings
    • Graph Neural Networks
  • Conclusion
    • Additional resources

The class will be a mix of interactive lectures followed by hands-on exercises for each part.
These exercises will be based on Colab backed jupyter notebooks.

LEVEL
Intermediate – Advanced

Prerequisite Knowledge
Prior exposure to Graph Databases or Machine Learning is helpful but not required.

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Behind the Tracks