{"product_id":"graphpowered-machine-learning-9781617295645","title":"Graph-Powered Machine Learning","description":"\u003cp\u003eUpgrade your machine learning models with graph-based algorithms  the perfect structure for complex and interlinked data.  Summary In Graph-Powered Machine Learning  you will learn:  The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing  recommendations  and fraud detection techniques Graph algorithms Working with Neo4J  Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. Youll 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 Negros extensive experience shines through in every chapter  as you learn from examples and concrete scenarios based on his work with real clients!  About the technology 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.  About the book Graph-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  youll 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.  Whats inside  Graphs in big data platforms Recommendations  natural language processing  fraud detection Graph algorithms Working with the Neo4J graph database  About the reader For readers comfortable with machine learning basics.  About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences  and holds a PhD in Computer Science.  Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs\u003c\/p\u003e","brand":"My Store","offers":[{"title":"Default Title","offer_id":67896211275829,"sku":"ByrdShop_1617295647","price":40.94,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0627\/8139\/0901\/files\/9781617295645.jpg?v=1793773402","url":"https:\/\/atxbooks.com\/products\/graphpowered-machine-learning-9781617295645","provider":"ATX Books","version":"1.0","type":"link"}