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Introduction to Graph Machine Learning

Gain insights into graph machine learning fundamentals, explore graph analytics, and delve into advanced topics like graph embedding and neural networks, enhancing your skills for research and practical applications.

Beginner

37 Lessons

Certificate of Completion

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This course includes

63 Playgrounds

7 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

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Course Overview

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 technique...Show More

TAKEAWAY SKILLS

Python

PyTorch basics

Graph

Machine Learning

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

Course Content

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

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

Course Author

Rohith Teja

Project

Measuring Causal Relations with Python

intermediate

1 hour 30 min

Project

Measuring Causal Relations with Python

Explore causal inference and discovery using Python, focusing on techniques for measuring causal relations between variables.

intermediate

1 hour 30 mins

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