Description
Graph-Powered Machine Learning is an essential resource for data scientists and machine learning engineers looking to harness the power of graph databases and graph algorithms. The book demonstrates how graphs can enhance machine learning models by capturing complex relationships and dependencies in data.
Written by Alessandro Nego, Dr. Webber, and Jim, this Manning publication covers practical implementations of graph-based ML systems, including knowledge graphs, recommendation engines, and link prediction. Readers will learn how to combine graph databases with popular ML frameworks to create more sophisticated and accurate models.
Whether you’re working with social networks, recommendation systems, or knowledge representation, this guide provides the tools and techniques needed to integrate graph technology into your machine learning pipeline effectively.
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