Graph-Powered Machine Learning (Final Release)

[voska89]

English | 2021 | ISBN: 1617295647 | 493 pages | True PDF + Code | 26.28 MB
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 Learningintroduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

https://github.com/alenegro81/gpml

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