Pytorch Geometric Courses and Certifications

Data Science Conference

Transforming DataFrames - Introduction to Graphs and Graph Neural Networks with PyTorch Geometric

Master graph structures and GNN implementation using PyTorch Geometric, from converting DataFrames to training models for enhanced predictive performance with interconnected data analysis.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

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Node Classification in Heterogeneous Graphs Using PyG and SBERT SentenceTransformers

Master heterogeneous graph analysis using PyG and SBERT for node classification, with hands-on implementation in PyTorch Geometric and practical applications in social networks and cybersecurity.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Machine Learning with Graphs - Fall 2019

Comprehensive exploration of machine learning with graphs, covering network properties, community structures, graph neural networks, and their applications in various domains.

• YouTube
• 1 day 4 hours
• Self-Paced
• Free Video

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Link Prediction with Graph Neural Networks Using DGL and PyG

Master link prediction in graph neural networks using DGL and PyG frameworks, implementing node connectivity prediction through practical code examples and negative sampling techniques.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

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Learn Graph Neural Networks - Code, Examples and Theory

Master Graph Neural Networks through hands-on coding, theory, and state-of-the-art libraries like PyTorch Geometric and DGL for deep learning on graph-structured data.

• YouTube
• 7 hours 49 minutes
• Self-Paced
• Free Video

Prodramp

Graph Neural Networks Implementation in Python

Learn Graph Neural Networks implementation in Python, covering graph representations, node embeddings, message passing, and practical applications using NetworkX and PyG libraries.

• YouTube
• 1 hour 59 minutes
• Self-Paced
• Free Video

Prodramp

Understanding Graph Neural Networks - Part 1

Comprehensive introduction to Graph Neural Networks, covering fundamentals, mathematics, and practical implementation using NetworkX and PyG. Ideal for beginners seeking hands-on experience with GNNs.

• YouTube
• 1 hour 55 minutes
• Self-Paced
• Free Video

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DSPy Combined with Graph Optimizer in PyG - Multi-Hop RAG Implementation

Explore DSPyG's innovative approach to multi-hop RAG implementation, combining DSPy with graph optimization for enhanced AI research capabilities using minimal computational resources.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

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Demonstrate-Search-Predict: LLM Pipeline to Retriever Model

Explore Stanford's innovative DSP methodology for integrating LLMs with retrieval models, featuring self-programmable pipelines and advanced graph-based architectures for complex NLP tasks.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

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Node Classification with Graph Convolutional Networks for Graph Machine Learning

Master Graph Convolutional Networks (GCN) implementation in PyG for node classification, featuring practical coding examples and theoretical foundations of spectral graph convolutions.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

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Uncover the Secrets of Graph Data - Training, Testing and Validation Graph Datasets with GraphML, PyG, and DGL

Dive into essential techniques for splitting graph datasets, avoiding common pitfalls in training, validation, and testing for machine learning applications with PyG and DGL frameworks.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

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Scaling GraphRAG to Planetary-wide Knowledge Graph

Discover how to scale GraphRAG systems to handle planetary-wide knowledge graphs, exploring advanced techniques for processing millions of documents and multi-path fusion approaches.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Data Science Festival

Advanced Python Libraries for Data Science

Explore advanced Python libraries for data science, focusing on scikit-learn, TensorFlow, and PyTorch Geometric. Learn implementation, methodologies, and practical applications through code exercises.

• YouTube
• 1 hour 45 minutes
• Self-Paced
• Free Video

Toronto Machine Learning Series (TMLS)

Kùzu - A Fast, Scalable Graph Database for Analytical Workloads

Discover how to build and analyze knowledge graphs using Kùzu, an open-source graph database. Learn practical implementations, from data transformation to running Cypher queries and training graph neural networks for advanced RAG systems.

• YouTube
• 1 hour 29 minutes
• Self-Paced
• Free Video

MLOps.community

Relational Foundation Models - Unlocking the Next Frontier of Enterprise AI

Discover how Relational Foundation Models revolutionize enterprise AI by reasoning over business relationships and connections, not just isolated data points.

• YouTube
• 49 minutes
• Self-Paced
• Free Video