The Ultimate Graph Neural Network Course

Name: The Ultimate Graph Neural Network Course
Rating: 3.9 (62 reviews)

Graph neural network course from beginner to advanced.

Created byThe3rd AI

Last updated 8/2023

English

What you'll learn

Mastering the Basics of Graphs and Graph Neural Networks (GNNs)
Implementing GNN Models from Scratch
Apply GNNs to Real-World Scenarios like Recommendation Systems
Complete modern-day GNN problems and challenges

Course content

7 sections • 25 lectures • 2h 18m total length

Introduction1:27
Overview2:58
By the end of this video you are going to get an idea of the course curriculum and what you can expect from the course.

Section Overview1:18
Section overview
Why Graphs?4:08
By the end of this lecture you would have an understanding of why graphs are becoming popular and why graphs are powerful.
Why Graph Learning?4:48
By the end of this lecture you would have a good understanding of why graphs learning is becoming popular and what challenges are being solved through graph learning.
Why Graph Neural Networks?3:45
By the end of this lecture you would get an idea of why graph neural networks are so powerful and are gaining popularity among data science enthusiasts.

Section Overview1:13
Section Overview
Introduction to Graph properties (coding)7:57
By the end of this lecture you would have a good understanding of the graph properties, also, you would be able to work with the graph datasets.
Introduction to Graph Concepts4:21
By the end of this lecture you would have a good understanding of some of the important concepts related to graphs.
Graph Algorithms (coding)5:37
By the end of this lecture you would have a good understanding of the graph algorithms. You would be able to code the most popular graph algorithms by yourself.

Section Overview0:59
Section overview.
Word2Vec Architecture3:08
By the end of this lecture you would have a good understanding of one of the most popular algorithms in NLP, that is Word2Vec. You would also have an understanding of how we can use these algorithms when dealing with graphs.
Skip Gram Architecture (Coding)6:00
By the end of this lecture you would have a good understanding of the Skip-Gram architecture.
Deepwalk and Randomwalk (Coding)3:24
By the end of this lecture you would have a good understanding of one of the most important architectures for graph learning, the DeepWalk architecture and it's building block, the Random Walk architecture. You would be able to code the random walk architecture after this lecture.
Implementing DeepWalk (Coding)9:08
By the end of this lecture you would be able to implement the DeepWalk architecture in python and you would be able to retrieve node embeddings as a result.

Section Overview1:14
Graph Datasets (Coding)8:19
By the end of this lecture you would be able to work with graph datasets. This lecture would teach you how graph datasets are different from tabular datasets and you would also look into some of the most popular graph datasets.
Node Classification using Vanilla Neural Network (Coding)8:16
By the end of this lecture you would be able to solve a node classification problem using a Vanilla Neural Network.
Implementing Vanilla Graph Neural Network from scratch (Coding)9:01
By the end of this lecture you would be able to solve a node classification challenge using a Vanilla Graph Neural Network. This is different from the last lecture as this one makes use of the graph data and the relationship between the nodes, instead of considering it as tabular data, like in the last lecture.

Section Overview1:01
Section Overview.
Graph Convolutional Neural Network4:19
By the end of this lecture you would have a good understanding of how Graph Convolutional Networks work.
Implementing Graph Convolutional Neural Network (Coding)6:25
By the end of this lecture you would be able to implement a Graph Convolutional Network on a graph dataset in python.
Predicting web traffic using Graph Convolutional Neural Network (Coding)11:22
This lecture is a small project on predicting web traffic using convolutional neural network.

Dataset Creation (Coding)16:52
By the end of this lecture you would be able to create a graph dataset using your own tabular dataset, so that you can use the power of graph neural networks.
Training a Graph Neural Network from scratch (Coding)11:01
By the end of this lecture you would be able to create a Movie Recommendation System using graphs and perform a Link Prediction task on the dataset that you just created in the previous lecture.

Requirements

You need to know Python in order to take this course. This course is intended for Data Science professionals and/or students who are aware of basic machine learning and deep learning concepts and libraries like pandas, numpy, PyTorch etc. To learn PyTorch, you can watch my free course "The Ultimate Computer Vision and Deep Learning Course" here on Udemy

Description

In this in-depth Udemy course on graph neural networks, you'll embark on a journey to master the art of extracting valuable insights from graph data. Through a carefully crafted curriculum, you'll first grasp the core principles of GNNs, understanding how they propagate information across nodes and edges to capture intricate relationships. With hands-on coding exercises, you'll gain the confidence to implement GNN models from scratch. You'll construct diverse architectures and witness the power of GNNs in action, including Graph Convolutional networks.

Graphs are ubiquitous in modern data analysis, from social networks to molecular structures, and GNNs are the cutting-edge tools to make sense of them. Through a carefully crafted curriculum, you'll first grasp the core principles of GNNs, understanding how they propagate information across nodes and edges to capture intricate relationships.

This course is more than just theory and coding. You'll learn to apply GNNs to real-world scenarios, solving problems like node classification and recommendation systems.

By the end of this course, you'll possess the skills to transform raw graph data into actionable insights. Whether you're a data scientist, machine learning practitioner, or curious learner, our Graph Neural Network course equips you with the tools to navigate the complex web of data all around us. Enroll now and unlock the potential of GNNs in your data analysis toolkit.

Who this course is for:

Data Science enthusiasts
Computer Science students
Machine Learning enthusiasts

The Ultimate Graph Neural Network Course

What you'll learn

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Course content

Introduction2 lectures • 4min

Getting Started4 lectures • 14min

Graph Theory for Neural Networks4 lectures • 19min

DeepWalk5 lectures • 23min

Vanilla Graph Neural Network4 lectures • 27min

Graph Convolutional Network4 lectures • 23min

Project- Creating a movie recommendation system2 lectures • 28min

Requirements

Description

Who this course is for: