Graph-Powered Machine LearningAuthor :
Paperback
Published : Monday 15 November 2021
Description
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs.
Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. Youll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, youll explore three end-to-end projects that illustrate architectures, best design practices,
optimization approaches, and common pitfalls.
Key Features
· The lifecycle of a machine learning project
· Three end-to-end applications
· Graphs in big data platforms
· Data source modeling
· Natural language processing, recommendations, and relevant search
· Optimization methods
Readers comfortable with machine learning basics.
About the technology
By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where its important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning.
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