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Introduction to Graph Machine Learning

Name: Introduction to Graph Machine Learning
Price: 59 USD
Author: Rohith Teja

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.

4.7

37 lessons

1 project

Created by Rohith Teja

What's included

37 lessons across 10 modules
7 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

37 Lessons7 Quizzes

About the Course

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

Getting Started

Introduction to Graph Theory

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.

Supervised and Unsupervised Graph ML

6 Lessons

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

Graph Neural Networks

4 Lessons

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

Link-Based Classification Using Graph Neural Networks

Project

Premium

Knowledge Graph

5 Lessons

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

Knowledge Graph Embeddings

4 Lessons

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

Case Study: Link Prediction on a Social Network Graph

3 Lessons

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

Case Study: Node Classification on a Biological Graph

3 Lessons

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

10.

Appendix

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.

About this course

WHY I WROTE 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

Learn more about Rohith

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