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.
Are you ready to attain mastery in graph machine learning? Graphs are ubiquitous and have diverse applications in various fields...Show More
WHAT YOU'LL LEARN
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
Familiarity with creating and manipulating graphs
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TAKEAWAY SKILLS
Content
2.
Introduction to Graph Theory
5 Lessons
Look at graph theory, types of graphs, data structures for representation, and visualization techniques.
3.
Graph Embeddings
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
6 Lessons
Grasp the fundamentals of supervised and unsupervised learning in graph machine learning.
5.
Graph Neural Networks
4 Lessons
Take a closer look at Graph Neural Networks' architectures, message passing, and practical applications.
6.
Knowledge Graph
5 Lessons
Tackle the construction, importance, issues, and embedding techniques of knowledge graphs.
7.
Knowledge Graph Embeddings
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
3 Lessons
Step through constructing and analyzing social network graphs for accurate link predictions.
9.
Case Study: Node Classification on a Biological Graph
3 Lessons
Solve challenges with node classification on a synthetic contact tracing network using GNNs.
Certificate of Completion
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