Graph-Powered Machine Learning

Alessandro Negro

ISBN 10: 1617295647 / ISBN 13: 9781617295645

Published by Manning Publications Nov 2021, 2021

About this Item

Neuware - 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. 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. 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 it's 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.

Seller Inventory # 9781617295645

Bibliographic Details

• Title: Graph-Powered Machine Learning
• Publisher: Manning Publications Nov 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 1617295647
• ISBN 13: 9781617295645
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 233W x 185H x 27D millimeters 233 millimeters width by 185 millimeters height by 27 millimeters depth
• Weight: 898 grams

About this title

Synopsis

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

About the Author

Alessandro Negro is immensely passionate about computer science, data research & specialize in natural language processing, recommendation engines, fraud detection, and graph-aided search.

After pursuing Computer Engineering academically & working in various capacities in the domain, he pursued a Ph.D in Interdisciplinary Science and Technology. With his interest in graph databases peaking, he founded a company called Reco4 which was an open source project developing a recommendation framework based on graph data sources.

Now, he is Chief Scientist at GraphAware. With clients such as LinkedIn, World Economic Forum, European Space Agency and Bank of America, GraphAware is singularly focused on helping clients gain a competitive edge by transforming their data into searchable, understandable & actionable knowledge. He spend his time leading research for Hume - GraphAware Knowledge Graph platform -, speaking at various conferences around the world and working on the next book.