Graph-Powered Machine Learning

by Alessandro Negro

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.Summary In Graph-Powered Machine Learning, you will     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. 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 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, 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 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 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  

Genres: Artificial Intelligence, Computer Science

856 pages, Kindle Edition

Published October 5, 2021

Reviews

[Mike Fowler · Rating 1 out of 5]

Frustrating book that is very repetitive and yet tantalising - you constantly feel like you’re on the verge on enlightenment only to be forestalled page after page. Long passages explaining the motivating scenarios seem to be reworded in each chapter. Examples are in Python, scikit-learn and Neo4j.

Caveat: This book is still in draft form and has been made available as part of Manning’s Early Access Program (MEAP). In particular this means that there has been no copy editing or seeming any proof reading.

[Alex Ott · Rating 4 out of 5]

I read prerelease version of the book...

good overview of the application of the graph approach to solving the problems traditionally solved with machine learning - recommendations, etc.

The biggest drawback from my side - sometimes it too wordy - you need to read a lot (sometimes is not completely related to the topic) before getting to the solving of the real problems.

[Pwyllugh · Rating 2 out of 5]

Didn't get to the good stuff and repeated information over and over again.