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Introduction to Graph Machine Learning
Gain insights into graph machine learning fundamentals. Explore graph analytics, and advanced topics like graph embedding and neural networks, enhancing your skills for practical applications.
4.8
37 Lessons
7h
Updated 1 month ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Familiarity with creating and manipulating graphs
- An understanding of the concepts of graph embedding and its various techniques
- Ability to formulate important graph analytics tasks such as node classification and link prediction
- Hands-on experience developing graph neural networks using PyTorch Geometric
- An understanding of knowledge graphs and different ways to generate their embeddings
- Comprehensive knowledge of graph machine learning concepts
Learning Roadmap
2.
Introduction to Graph Theory
Introduction to Graph Theory
Look at graph theory, types of graphs, data structures for representation, and visualization techniques.
3.
Graph Embeddings
Graph Embeddings
5 Lessons
5 Lessons
Break apart the methods and techniques for generating graph embeddings using matrix factorization, random walks, and neural networks.
4.
Supervised and Unsupervised Graph ML
Supervised and Unsupervised Graph ML
6 Lessons
6 Lessons
Grasp the fundamentals of supervised and unsupervised learning in graph machine learning.
5.
Graph Neural Networks
Graph Neural Networks
4 Lessons
4 Lessons
Take a closer look at Graph Neural Networks' architectures, message passing, and practical applications.
6.
Knowledge Graph
Knowledge Graph
5 Lessons
5 Lessons
Tackle the construction, importance, issues, and embedding techniques of knowledge graphs.
7.
Knowledge Graph Embeddings
Knowledge Graph Embeddings
4 Lessons
4 Lessons
Master the steps to create knowledge graph embeddings using translation, factorization, and neural network methods.
8.
Case Study: Link Prediction on a Social Network Graph
Case Study: Link Prediction on a Social Network Graph
3 Lessons
3 Lessons
Step through constructing and analyzing social network graphs for accurate link predictions.
9.
Case Study: Node Classification on a Biological Graph
Case Study: Node Classification on a Biological Graph
3 Lessons
3 Lessons
Solve challenges with node classification on a synthetic contact tracing network using GNNs.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Are you ready to attain mastery in graph machine learning? Graphs are ubiquitous and have diverse applications in various fields. In this introductory course, you will learn the fundamentals of graph machine learning so that you’re able to work with different types of graphs, state-of-the-art graph machine learning techniques, and various graph analytics tasks.
The course begins with the basics of graphs and gradually progresses to more advanced topics, including graph embedding and its different techniques and different use cases built on knowledge graphs.
By the end of the course, you will have gained hands-on experience in creating and manipulating graphs, as well as a comprehensive understanding of knowledge graphs and graph neural networks. This course will equip you with the skills and knowledge to step into advanced graph machine learning topics for research or practical applications and take your career to the next level.
ABOUT THE AUTHOR
Rohith Teja
Data Scientist and an expert in Graph Machine Learning
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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