HomeCoursesIntroduction to Graph Machine Learning

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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.

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Overview

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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

TAKEAWAY SKILLS

Python

PyTorch basics

Graph

Machine Learning

Content

37 Lessons7 Quizzes

About the Course

1 Lessons

Get familiar with graph machine learning, its concepts, techniques, and coding applications.

Getting Started

Introduction to Graph Theory

5 Lessons

Look at graph theory, types of graphs, data structures for representation, and visualization techniques.

What is a Graph?

Types of Graphs

How to Represent Graphs as Data Structures

Graph Network Visualization

Quiz: Graph Theory

Graph Embeddings

5 Lessons

Break apart the methods and techniques for generating graph embeddings using matrix factorization, random walks, and neural networks.

What are Graph Embeddings?

Matrix Factorization-Based Approach

Random Walk-Based Approach

Neural Network-Based Approach

Quiz: Graph Embeddings

Supervised and Unsupervised Graph ML

6 Lessons

Grasp the fundamentals of supervised and unsupervised learning in graph machine learning.

What's Next After Learning Embeddings?

Node Classification (Supervised)

Link Prediction (Supervised)

Graph Classification (Supervised)

Graph Clustering and Community Detection (Unsupervised)

Quiz: Graph Machine Learning Tasks

Graph Neural Networks

4 Lessons

Take a closer look at Graph Neural Networks' architectures, message passing, and practical applications.

What are Graph Neural Networks?

Neural Message Passing

Types of Graph Neural Networks

Quiz: Graph Neural Networks

Link-Based Classification Using Graph Neural Networks

Project

Knowledge Graph

5 Lessons

Tackle the construction, importance, issues, and embedding techniques of knowledge graphs.

What is a Knowledge Graph?

Knowledge Graph Construction

Importance of Knowledge Graphs and Their Issues

Knowledge Graph Embeddings

Quiz: Knowledge Graphs

Knowledge Graph Embeddings

4 Lessons

Master the steps to create knowledge graph embeddings using translation, factorization, and neural network methods.

Translation-Based Methods

Factorization-Based Methods

Neural Network-Based Methods

Quiz: Knowledge Graph Embeddings

Case Study: Link Prediction on a Social Network Graph

3 Lessons

Step through constructing and analyzing social network graphs for accurate link predictions.

Problem Description

Approach to the Problem

Coding the Solution

Case Study: Node Classification on a Biological Graph

3 Lessons

Solve challenges with node classification on a synthetic contact tracing network using GNNs.

Problem Description

Approach to the Problem

Coding the Solution

10.

Appendix

1 Lessons

Examine essential Python libraries and their versions for graph machine learning.

Python Libraries

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Rohith Teja

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