{"product_id":"9781617295645","title":"Graph-Powered Machine Learning","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNego, Alessandro\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e9\/28\/2021\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eUpgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eSummary\u003cbr\u003eIn \u003ci\u003eGraph-Powered Machine Learning\u003c\/i\u003e, you will learn:\u003cbr\u003e\u003cbr\u003eThe lifecycle of a machine learning project\u003cbr\u003eGraphs in big data platforms\u003cbr\u003eData source modeling using graphs\u003cbr\u003eGraph-based natural language processing, recommendations, and fraud detection techniques\u003cbr\u003eGraph algorithms\u003cbr\u003eWorking with Neo4J\u003cbr\u003e\u003cbr\u003e\u003ci\u003eGraph-Powered Machine Learning\u003c\/i\u003e 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!\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eIdentifying 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGraph-Powered Machine Learning\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eWhat's inside\u003cbr\u003e\u003cbr\u003eGraphs in big data platforms\u003cbr\u003eRecommendations, natural language processing, fraud detection\u003cbr\u003eGraph algorithms\u003cbr\u003eWorking with the Neo4J graph database\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eFor readers comfortable with machine learning basics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAlessandro Negro\u003c\/b\u003e is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART 1 INTRODUCTION\u003cbr\u003e1 Machine learning and graphs: An introduction\u003cbr\u003e2 Graph data engineering\u003cbr\u003e3 Graphs in machine learning applications\u003cbr\u003ePART 2 RECOMMENDATIONS\u003cbr\u003e4 Content-based recommendations\u003cbr\u003e5 Collaborative filtering\u003cbr\u003e6 Session-based recommendations\u003cbr\u003e7 Context-aware and hybrid recommendations\u003cbr\u003ePART 3 FIGHTING FRAUD\u003cbr\u003e8 Basic approaches to graph-powered fraud detection\u003cbr\u003e9 Proximity-based algorithms\u003cbr\u003e10 Social network analysis against fraud\u003cbr\u003ePART 4 TAMING TEXT WITH GRAPHS\u003cbr\u003e11 Graph-based natural language processing\u003cbr\u003e12 Knowledge graphs","brand":"Manning","offers":[{"title":"Default Title","offer_id":48176691314943,"sku":"9781617295645","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_0e05ad8b-17b3-4152-9885-d04d63c09805.jpg?v=1771351504","url":"https:\/\/massivebookshop.com\/products\/9781617295645","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}