Graph Machine Learning Courses and Certifications

Valence Labs

DGL 1.0 - Empowering Graph Machine Learning for Everyone

Explore Deep Graph Library 1.0's comprehensive support for Graph Machine Learning, featuring DGL Sparse for advanced models and scalability improvements for real-world applications.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

Discover AI

Node and Edge Classification with GraphSAGE for Graph Machine Learning

Master node and edge classification in GraphML using GraphSAGE, covering DGL implementation, heterogeneous graphs, and practical PyTorch examples for effective graph neural network development.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Data Science Festival

Graph Machine Learning: Enhancing ML with Network Graphs and Embeddings

Explore graph-based machine learning techniques to enhance data analysis and uncover hidden relationships in your datasets. Learn to leverage network graphs for improved model performance.

• YouTube
• 59 minutes
• On-Demand
• Free Video

Toronto Machine Learning Series (TMLS)

Learning Embedded Representation of Stock Correlation Matrix Using Graph Machine Learning

Explore embedded representation of stock correlation matrices using graph machine learning. Learn to analyze non-linear relationships among financial instruments for investment processes and risk management.

• YouTube
• 34 minutes
• On-Demand
• Free Video

IMSA

Discrete Curvature and Applications in Graph Machine Learning

Exploring discrete curvature in graph machine learning, focusing on applications and theoretical foundations for analyzing complex network structures and improving graph-based algorithms.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

MLOps World: Machine Learning in Production

Uncovering Financial Fraud with Graph Machine Learning at Scale

Discover how graph machine learning transforms fraud detection by analyzing entity relationships and coordinated suspicious activities at enterprise scale.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Simons Institute

Graph Machine Learning and Random Graph Models

Explore graph machine learning and GNN behavior on large random graphs through geometric models, convergence analysis, and stability insights for deep architectures.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Learning Graph Neural Networks

Learn about the use cases of graph modeling and find out how to train graph neural networks and evaluate its results.

• LinkedIn Learning
• 2 hours 13 minutes
• On-Demand
• Free Trial Available

Discover AI

Uncover the Secrets of Graph Data - Training, Testing and Validation Graph Datasets with GraphML, PyG, and DGL

Dive into essential techniques for splitting graph datasets, avoiding common pitfalls in training, validation, and testing for machine learning applications with PyG and DGL frameworks.

• YouTube
• 17 minutes
• On-Demand
• Free Video

Simons Institute

Toward Universal Graph Representations - Foundations and Frontiers of Graph Foundation Models

Explore graph foundation models and universal representations, examining positional vs structural embeddings, invariances in statistical tests, and applications to algorithmic reasoning in network optimization.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Discover AI

Node Classification with Graph Convolutional Networks for Graph Machine Learning

Master Graph Convolutional Networks (GCN) implementation in PyG for node classification, featuring practical coding examples and theoretical foundations of spectral graph convolutions.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Fields Institute

Mutual Funds Similarity Using Graph Machine Learning

Explore mutual fund similarity using graph machine learning techniques. Learn about bipartite networks, clustering coefficients, and homophily in financial data analysis.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

IMSA

Szemerédi Regularity Lemma in Graph Machine Learning

Exploring the Szemerédi regularity lemma's application in graph machine learning, focusing on its theoretical foundations and practical implications for AI-driven graph analysis.

• YouTube
• 53 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 7

Explore advanced machine learning techniques for high energy physics, including Deepsets, Graph Neural Networks, and Transformers, to analyze complex particle physics data.

• YouTube
• 1 hour 26 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Explainable Structured Machine Learning in Similarity, Graph and Transformer Models

Explores techniques for explaining complex machine learning models, focusing on similarity, graph, and transformer structures. Discusses applications in knowledge evolution, gender bias, and task-solving.

• YouTube
• 53 minutes
• On-Demand
• Free Video