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 DescriptionNeo4j, 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
predictions such as link prediction Explore the latest version of
Neo4j to build a graph data science pipeline Run a scikit-learn
prediction algorithm with graph data Train a predictive embedding
algorithm in GDS and manage the model store Who this book is forIf
you’re a data scientist or data professional with a foundation in
the basics of Neo4j and are now ready to understand how to build
advanced analytics solutions, you’ll find this graph data science
book useful. Familiarity with the major components of a data
science project in Python and Neo4j is necessary to follow the
concepts covered in this book.
General
Imprint: |
Packt Publishing Limited
|
Country of origin: |
United Kingdom |
Release date: |
2023 |
Authors: |
Estelle Scifo
|
Dimensions: |
93 x 75mm (L x W) |
Pages: |
288 |
ISBN-13: |
978-1-80461-274-3 |
Categories: |
Books
|
LSN: |
1-80461-274-X |
Barcode: |
9781804612743 |
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