Graph Neural Network (GNN) Course

12 Lesson

30 Hours

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Graph Neural Network (GNN) Course helps you learn how to build AI models that work with graph-structured data, where relationships between entities are as important as the data itself. In this course, you will learn graph fundamentals, graph embeddings, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), GraphSAGE, and graph classification using PyTorch Geometric and DGL. Through hands-on projects, you will gain practical experience building Graph Neural Network models for real-world applications such as recommendation systems, fraud detection, knowledge graphs, social network analysis, and molecular property prediction.

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Overview

Prerequisites

Basic knowledge of Python programming
Understanding of machine learning fundamentals, including supervised and unsupervised learning
Familiarity with deep learning concepts such as neural networks, tensors, activation functions, and backpropagation
Knowledge of graph basics, including nodes and edges, is helpful but not mandatory
Experience with PyTorch is recommended, but not mandatory

What Will You Learn

Introduction to Graph Neural Networks (GNNs)
Graph Theory Fundamentals
Graph Data Structures and Representations
Graph Representation Learning
Node, Edge, and Graph Embeddings
Message Passing Neural Networks
Graph Convolutional Networks (GCNs)
Graph Attention Networks (GATs)
GraphSAGE and Graph Isomorphism Networks (GINs)
Heterogeneous Graph Neural Networks
Node Classification and Graph Classification
Link Prediction Techniques
Working with PyTorch Geometric (PyG)
Building GNN Models with Deep Graph Library (DGL)
Training, Evaluating, and Optimizing GNN Models
Graph Data Preprocessing and Feature Engineering

Key Features

100% Money-Back Guarantee
Flexible Schedule
24 X 7 Lifetime Support
Get Certified & Get Placed
Experienced & Professional Trainers
1 On 1 Training Option Available

Course Curriculum

Lesson 1 - Introduction to Graph Neural Networks

1. What are Graph Neural Networks?

2. Applications of GNNs

3. Graph-based machine learning vs. traditional machine learning

4. GNN use cases across industries

Lesson 2 - Graph Fundamentals

1. Graph theory basics

2. Nodes, edges, and graph properties

3. Directed and undirected graphs

4. Weighted and unweighted graphs

5. Homogeneous and heterogeneous graphs

6. Graph representations (Adjacency Matrix and Edge List)

Lesson 3 - Python and Deep Learning Foundations

1. Review of Python for GNN development

2. Introduction to PyTorch

3. Tensors and tensor operations

4. Automatic differentiation

5. Building basic neural networks

Lesson 4 - Graph Representation Learning

1. Node embeddings

2. Edge embeddings

3. Graph embeddings

4. Feature engineering for graph data

5. Message passing framework

Lesson 5 - Graph Neural Network Architectures

1. Graph Convolutional Networks (GCN)

2. Graph Attention Networks (GAT)

3. GraphSAGE

4. Graph Isomorphism Network (GIN)

5. Relational Graph Convolutional Networks (R-GCN)

Lesson 6 - Working with PyTorch Geometric (PyG)

1. Installing and configuring PyTorch Geometric

2. Creating graph datasets

3. Data loaders and graph transforms

4. Building and training GNN models

5. Model evaluation

Lesson 7 - Deep Graph Library (DGL)

1. Introduction to DGL

2. Graph creation and manipulation

3. Building GNN models with DGL

4. Training and inference workflows

Lesson 8 - Graph Learning Tasks

1. Node classification

2. Graph classification

3. Link prediction

4. Community detection

5. Recommendation systems

Lesson 9 - Training and Optimizing GNN Models

1. Loss functions

2. Optimizers

3. Regularization techniques

4. Hyperparameter tuning

5. Handling large-scale graphs

Lesson 10 - Explainability and Model Evaluation

1. Evaluating GNN performance

2. Model interpretability

3. Explainable AI (XAI) for Graph Neural Networks

4. Common challenges and best practices

Lesson 11 - Real-World Projects

1. Fraud detection using Graph Neural Networks

2. Social network analysis

3. Knowledge graph applications

4. Recommendation system using GNNs

5. Molecular property prediction

Lesson 12 - Deployment and Next Steps

1. Saving and loading trained models

2. Deploying GNN models

3. Scaling graph learning pipelines

4. Emerging trends in Graph Neural Networks

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Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 30 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 19 Jul 2026
Weekday Batch 20 Jul 2026
Weekend Batch 25 Jul 2026

1 ON 1 Training

US $ 899.00

100% Money Back Guarantee

Duration : 30 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 19 Jul 2026
Weekday Batch 20 Jul 2026
Weekend Batch 25 Jul 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

Graph Neural Network (GNN) Certification

Complete the Graph Neural Network Training and receive an igmGuru course completion certificate. This certificate highlights your practical skills in designing, training, and evaluating Graph Neural Network models for real-world machine learning applications using industry-standard tools and frameworks.

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Graph Neural Network (GNN) Course

Overview

Prerequisites

What Will You Learn

Key Features

Course Curriculum

Lesson 1 - Introduction to Graph Neural Networks

Lesson 2 - Graph Fundamentals

Lesson 3 - Python and Deep Learning Foundations

Lesson 4 - Graph Representation Learning

Lesson 5 - Graph Neural Network Architectures

Lesson 6 - Working with PyTorch Geometric (PyG)

Lesson 7 - Deep Graph Library (DGL)

Lesson 8 - Graph Learning Tasks

Lesson 9 - Training and Optimizing GNN Models

Lesson 10 - Explainability and Model Evaluation

Lesson 11 - Real-World Projects

Lesson 12 - Deployment and Next Steps

Request For Live Demo Class

Course Fees

Online Class Room Program

Classes Starting From

1 ON 1 Training

Classes Starting From

Corporate Training

Trusted By Top Companies Worldwide

Want to know Today's Offer

Graph Neural Network (GNN) Certification

Reviews

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