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<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.</b> <p/>Summary<br> In <i>Graph-Powered Machine Learning</i>, you will learn: <p/> The lifecycle of a machine learning project<br> Graphs in big data platforms<br> Data source modeling using graphs<br> Graph-based natural language processing, recommendations, and fraud detection techniques<br> Graph algorithms<br> Working with Neo4J <p/><i>Graph-Powered Machine Learning</i> teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! <p/>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. <p/>About the technology<br> Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. <p/>About the book<br> <i>Graph-Powered Machine Learning</i> teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. <p/>What's inside <p/> Graphs in big data platforms<br> Recommendations, natural language processing, fraud detection<br> Graph algorithms<br> Working with the Neo4J graph database <p/>About the reader<br> For readers comfortable with machine learning basics. <p/>About the author<br> <b>Alessandro Negro</b> is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. <p/>Table of Contents<br> PART 1 INTRODUCTION<br> 1 Machine learning and graphs: An introduction<br> 2 Graph data engineering<br> 3 Graphs in machine learning applications<br> PART 2 RECOMMENDATIONS<br> 4 Content-based recommendations<br> 5 Collaborative filtering<br> 6 Session-based recommendations<br> 7 Context-aware and hybrid recommendations<br> PART 3 FIGHTING FRAUD<br> 8 Basic approaches to graph-powered fraud detection<br> 9 Proximity-based algorithms<br> 10 Social network analysis against fraud<br> PART 4 TAMING TEXT WITH GRAPHS<br> 11 Graph-based natural language processing<br> 12 Knowledge graphs<p/><br></br><p><b> About the Author </b></p></br></br><b>Alessandro Negro</b> is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

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