Study message passing, graph classification, and relational graph prediction.
Explore graph-attention foundations, neural message passing, node and graph classification, and multi-relational knowledge-graph completion. The sequence connects graph structure and feature aggregation to prediction tasks, then asks how augmentation and geometric assumptions affect generalization. Learners make graph construction choices explicit and design leakage-aware evaluation; the English-audio videos are paired with independently written study materials, not transcripts.
Describe a message-passing layer as neighborhood information aggregation followed by node representation updates. Distinguish node-level, graph-level, and relational link-prediction tasks and choose an appropriate split. Evaluate how graph construction, class imbalance, augmentation, and relation geometry can change a GNN result.
Prior exposure to neural networks, linear algebra, and supervised classification is recommended. Familiarity with adjacency matrices, embeddings, and train/validation/test evaluation will help; no graph library is required.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.