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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 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!
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 graphs: 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
Summary
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!
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 graphs: 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
Tietoja kirjoittajasta
Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.
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