Neo4j Graph Data Science: Node Classification Pipelines

Graph databases capture rich relationships between data points, but leveraging this connectivity for machine learning requires specialized techniques. This text-based guide teaches you how to transform connected data into predictive insights using graph-native machine learning workflows. You will gain the practical knowledge required to construct end-to-end node classification models directly inside graph environments, from data projection to feature extraction and model validation. What you'll learn: - Understand core graph terminology, topology concepts, and foundational principles of graph data science. - Configure and manage in-memory graph projections for machine learning workloads. - Extract meaningful features using centrality algorithms, community detection, and node embeddings. - Build and configure supervised node classification pipelines using modern model selection techniques. - Train, evaluate, and tune predictive models with cross-validation and relevant performance metrics. - Apply predicted labels back to graph databases for downstream analytics and query workflows. The course begins with fundamental graph concepts and syntax before guiding you through feature engineering, pipeline configuration, and hands-on prediction scenarios. This course is designed for beginners in graph data science, requiring no prior experience with graph-native machine learning. Start reading today to expand your data analysis toolkit with graph-powered predictive modeling.