January 2023
Intermediate to advanced
288 pages
6h 20m
English
Overview
"Graph Data Science with Neo4j" teaches you how to utilize Neo4j 5 and its Graph Data Science Library 2.0 for analyzing and making predictions with graph data. By integrating graph algorithms into actionable machine learning pipelines using Python, you'll harness the power of graph-based data models.
What this Book will help me do
- Query and manipulate graph data using Cypher in Neo4j.
- Design and implement graph datasets using your data and public sources.
- Utilize graph-specific algorithms for tasks such as link prediction.
- Integrate graph data science pipelines into machine learning projects.
- Understand and apply predictive modeling using the GDS Library.
Author(s)
None Scifo, the author of "Graph Data Science with Neo4j," is an experienced data scientist with expertise in graph databases and advanced machine learning techniques. Their technical approach combines practical implementation with clear, step-by-step guidance to provide readers the skills they need to excel.
Who is it for?
This book is ideal for data scientists and analysts familiar with basic Neo4j concepts and Python-based data science workflows who wish to deepen their skills in graph algorithms and machine learning integration. It is particularly suited for professionals aiming to advance their expertise in graph data science for practical applications.
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