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Graph-Powered Machine Learning
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.
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!
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.
About the Audiobook
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 audiobook, 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.
About the Author
Alessandro Negro is the chief scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
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!
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.
About the Audiobook
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 audiobook, 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.
About the Author
Alessandro Negro is the chief scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Audible Audio
Published March 10, 2022
3 people are currently reading
47 people want to read
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Displaying 1 - 3 of 3 reviews
July 28, 2021
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.
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.
November 28, 2020
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.
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.
April 18, 2025
Didn't get to the good stuff and repeated information over and over again.
Displaying 1 - 3 of 3 reviews