Certificate of Completion
Introduction to Graph Neural NetworksPresented To
Md Asif Sajeed
Certificate ID
7jf9-bcuy-mgfk-3auh
Issued Date
August 7, 2026
Description
This course provides a technical introduction to Graph Neural Networks (GNNs), a class of models designed to perform inference on data structured as graphs. You will learn the principles behind how GNNs operate, from message passing mechanisms to specific architectures like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). The material covers the full process of building a GNN model: representing graph data, defining the network architecture, training the model, and evaluating its performance on common graph-related tasks. Practical sections focus on implementation using the PyTorch Geometric library.
Certificate Details
Course Level:
Professional
Issued Date:
August 7, 2026
Certificate ID:
7jf9-bcuy-mgfk-3auh
Learning Outcomes
- Represent graph-structured data for machine learning applications using adjacency and feature matrices.
- Explain the core message passing mechanism that forms the basis of most GNN architectures.
- Differentiate and implement foundational GNN models, including GCNs, GraphSAGE, and GATs.
- Construct a complete training and evaluation pipeline for a GNN on tasks like node classification.
- Build and train GNNs efficiently using the PyTorch Geometric library.