Graph Machine Learning Tasks and Concepts

Analyzing networked data requires a shift from traditional grid-like machine learning to graph-structured paradigms. Understanding how to frame problems as graph tasks is essential for modern data science and AI applications. This text-based course guides you through the fundamental tasks of graph machine learning, helping you build a strong conceptual foundation. You will learn to identify, structure, and evaluate graph-based problems, preparing you to apply these methodologies to real-world network data. What you'll learn: Understand the foundational representations of graph data, including adjacency matrices and node features; Differentiate between node-level, edge-level, and graph-level machine learning tasks; Explore the mechanics of message passing and Graph Neural Networks (GNNs); Apply graph machine learning concepts to real-world scenarios like link prediction and community detection; Evaluate model performance on graph datasets using appropriate metrics and validation strategies; Practice conceptual problem-solving through comprehensive written exercises and scenarios. The course begins with essential graph terminology and structural representations before diving deep into specific ML tasks, message-passing algorithms, and evaluation techniques. Designed for beginners in machine learning who want to expand their skills into graph-structured data, this course requires no prior experience with graph algorithms. Start reading today to unlock the power of graph-structured machine learning.