Graph-Powered Machine Learning

Manning Publications

Unlock the power of graph-based algorithms for advanced machine learning. This book teaches you to leverage the structure of complex, interlinked data to build superior machine learning applications. Discover how to implement graph-based techniques for natural language processing, recommendation systems, and fraud detection. Explore end-to-end projects and best design practices. Perfect for those comfortable with machine learning basics, this guide provides a deep dive into graph data engineering, algorithm design, and working with graph databases like Neo4J. Key topics covered: * The machine learning project lifecycle * Graphs in big data platforms * Data source modeling using graphs * Graph-based NLP, recommendations, and fraud detection * Graph algorithms * Working with Neo4J Product Summary: Graph-Powered Machine Learning, authored by Alessandro Negro, is a 496-page paperback book published by Manning Publications. This English version delves into advanced machine learning concepts using graph-based algorithms, suitable for readers with a foundation in machine learning.