(Ebook) Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project by Scifo, Estelle ISBN 9781804612743, 180461274X, 9781804612763, 180461234X

(Ebook) Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project by Scifo, Estelle ISBN 9781804612743, 180461274X, 9781804612763, 180461234X

Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning Purchase of the print or Kindle book includes a free PDF eBook Key Features: Extract meaningful information from graph data with Neo4j's latest version 5 Use Graph Algorithms into a regular Machine Learning pipeline in Python Learn the core principles of the Graph Data Science Library to make predictions and create data science pipelines. Book Description: Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You'll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you'll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you'll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you'll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions. What You Will Learn: Use the Cypher query language to query graph databases such as Neo4j Build graph datasets from your own data and public knowledge graphs Make graph-specific