Graph-Powered Machine Learning
- 12h 27m 28s
- Alessandro Negro, Julie Brierley
- Manning Publications
- 2021
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.
In Graph-Powered Machine Learning, you will learn:
- 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.
In this Audiobook
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Chapter 1 - Machine learning and graphs: An introduction
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Chapter 2 - Graph data engineering
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Chapter 3 - Graphs in machine learning applications
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Chapter 4 - Content-based recommendations
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Chapter 5 - Collaborative filtering
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Chapter 6 - Session-based recommendations
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Chapter 7 - Context-aware and hybrid recommendations
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Chapter 8 - Basic approaches to graph-powered fraud detection
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Chapter 9 - Proximity-based algorithms
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Chapter 10 - Social network analysis against fraud
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Chapter 11 - Graph-based natural language processing
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Chapter 12 - Knowledge graphs
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