Practical approaches to leveraging graph data science to solve
real-world challenges. Key Features ● Explore the fundamentals of
graph data science, its importance, and applications. ● Learn how to
set up Python and Neo4j environments for graph data analysis. ●
Discover techniques to visualize complex graph networks for better
understanding. Book Description Graph Data Science with Python and
Neo4j is your ultimate guide to unleashing the potential of graph data
science by blending Python's robust capabilities with Neo4j's
innovative graph database technology. From fundamental concepts to
advanced analytics and machine learning techniques, you'll learn how
to leverage interconnected data to drive actionable insights. Beyond
theory, this book focuses on practical application, providing you with
the hands-on skills needed to tackle real-world challenges. You'll
explore cutting-edge integrations with Large Language Models (LLMs)
like ChatGPT to build advanced recommendation systems. With intuitive
frameworks and interconnected data strategies, you'll elevate your
analytical prowess. This book offers a straightforward approach to
mastering graph data science. With detailed explanations, real-world
examples, and a dedicated GitHub repository filled with code examples,
this book is an indispensable resource for anyone seeking to enhance
their data practices with graph technology. Join us on this
transformative journey across various industries, and unlock new,
actionable insights from your data. What you will learn ● Set up and
utilize Python and Neo4j environments effectively for graph analysis.
● Import and manipulate data within the Neo4j graph database using
Cypher Query Language. ● Visualize complex graph networks to gain
insights into data relationships and patterns. ● Enhance data
analysis by integrating ChatGPT for context-rich data enrichment. ●
Explore advanced topics including Neo4j vector indexing and
Retrieval-Augmented Generation (RAG). ● Develop recommendation
engines leveraging graph embeddings for personalized suggestions. ●
Build and deploy recommendation systems and fraud detection models
using graph techniques. ● Gain insights into the future trends and
advancements shaping the field of graph data science. Table of
Contents 1. Introduction to Graph Data Science 2. Getting Started with
Python and Neo4j 3. Import Data into the Neo4j Graph Database 4.
Cypher Query Language 5. Visualizing Graph Networks 6. Enriching Neo4j
Data with ChatGPT 7. Neo4j Vector Index and Retrieval-Augmented
Generation (RAG) 8. Graph Algorithms in Neo4j 9. Recommendation
Engines Using Embeddings 10. Fraud Detection CLOSING SUMMARY The
Future of Graph Data Science Index About the Author Timothy (Tim)
Eastridge is an A.I. consultant known for his innovation and thought
leadership in integrating knowledge graphs with Generative AI and
Large Language Models (LLMs). His expertise in extracting actionable
insights from complex datasets positions him as a leader in
transforming data into understandable formats. Tim’s innovative
solutions have resulted in billions of dollars of suspicious activity
reports (SARs) for a major bank related to the Paycheck Protection
Program (PPP). He continues this work as a consultant to the Pandemic
Response Accountability Committee (PRAC), leading the team in the
identification, prioritization, and indictment of fraudsters using a
combination of unsupervised machine learning and recommendation
systems.
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Produktdetaljer
ISBN
9788197081965
Publisert
2024
Utgave
1. utgave
Utgiver
Vendor
Orange Education Pvt. Ltd.
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter