Curriculum

14 Chapters · 14 Lessons

  1. 1

    Book Preview

    1. Graph Data Science with Python and Neo4j Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Introduction to Graph Data Science

    1. (Included in full purchase)

      Introduction to Graph Data Science

  4. 4

    Chapter 2 : Getting Started with Python and Neo4j

    1. (Included in full purchase)

      Getting Started with Python and Neo4j

  5. 5

    Chapter 3 : Import Data into the Neo4j Graph Database

    1. (Included in full purchase)

      Import Data into the Neo4j Graph Database

  6. 6

    Chapter 4 : Cypher Query Language

    1. (Included in full purchase)

      Cypher Query Language

  7. 7

    Chapter 5 : Visualizing Graph Networks

    1. (Included in full purchase)

      Visualizing Graph Networks

  8. 8

    Chapter 6 : Enriching Neo4j Data with ChatGPT

    1. (Included in full purchase)

      Enriching Neo4j Data with ChatGPT

  9. 9

    Chapter 7 : Neo4j Vector Index and Retrieval-Augmented Generation (RAG)

    1. (Included in full purchase)

      Neo4j Vector Index and Retrieval-Augmented Generation (RAG)

  10. 10

    Chapter 8 : Graph Algorithms in Neo4j

    1. (Included in full purchase)

      Graph Algorithms in Neo4j

  11. 11

    Chapter 9 : Recommendation Engines Using Embeddings

    1. (Included in full purchase)

      Recommendation Engines Using Embeddings

  12. 12

    Chapter 10 : Fraud Detection

    1. (Included in full purchase)

      Fraud Detection

  13. 13

    CLOSING SUMMARY

    1. (Included in full purchase)

      The Future of Graph Data Science

  14. 14

    Index

    1. (Included in full purchase)

      Index

About the course

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.

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.