{ "item_title" : "Graph-Powered Machine Learning", "item_author" : [" Alessandro Nego "], "item_description" : "Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. SummaryIn Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning projectGraphs in big data platformsData source modeling using graphsGraph-based natural language processing, recommendations, and fraud detection techniquesGraph algorithmsWorking with Neo4J Graph-Powered Machine Learning 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 Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technologyIdentifying 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. About the bookGraph-Powered Machine Learning 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. What's inside Graphs in big data platformsRecommendations, natural language processing, fraud detectionGraph algorithmsWorking with the Neo4J graph database About the readerFor readers comfortable with machine learning basics. About the authorAlessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of ContentsPART 1 INTRODUCTION1 Machine learning and graphs: An introduction2 Graph data engineering3 Graphs in machine learning applicationsPART 2 RECOMMENDATIONS4 Content-based recommendations5 Collaborative filtering6 Session-based recommendations7 Context-aware and hybrid recommendationsPART 3 FIGHTING FRAUD8 Basic approaches to graph-powered fraud detection9 Proximity-based algorithms10 Social network analysis against fraudPART 4 TAMING TEXT WITH GRAPHS11 Graph-based natural language processing12 Knowledge graphs", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/61/729/564/1617295647_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

Overview

Customers Also Bought

Details