June 2021
Intermediate to advanced
338 pages
7h 48m
English
Overview
Graph Machine Learning introduces you to processing and analyzing graph data using machine learning techniques. You'll explore how to harness the relationships within graph structures for applications ranging from social networks to financial systems, enabling predictive modeling and analytics tasks.
What this Book will help me do
- Understand and apply graph machine learning techniques for a variety of data models.
- Learn to implement supervised and unsupervised algorithms for graph embeddings.
- Apply graph analytics to real-life scenarios like social networks and financial transactions.
- Build and scale applications that utilize graph-based data representations.
- Master essential graph theory concepts and their use in machine learning pipelines.
Author(s)
Claudio Stamile, a researcher and data scientist, Aldo Marzullo, an expert in graph algorithms, and Enrico Deusebio, a consultant in machine learning applications, combine their expertise to bring this comprehensive guide to readers. Their practical approach ensures the content is immediately applicable to real-world challenges.
Who is it for?
This book is tailored for data scientists, analysts, and software developers looking to understand and leverage graph structures in machine learning. Readers should ideally have a foundational knowledge of Python programming, basic graph concepts, and machine learning principles. It's perfect for professionals seeking to expand their analytics capabilities into graph-based data processing.
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