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Estelle Scifo
Graph Data Science with Neo4J
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1825962Graph Data Science with Neo4Jhttps://www.gandhi.com.mx/graph-data-science-with-neo4j/phttps://gandhi.vtexassets.com/arquivos/ids/201480/0cbdfcd6-10c3-4b30-a77c-ebac8c8bf48f.jpg?v=638333788141230000665739MXNPackt PublishingInStock/Ebooks/1794214Graph Data Science with Neo4J665739https://www.gandhi.com.mx/graph-data-science-with-neo4j/phttps://gandhi.vtexassets.com/arquivos/ids/201480/0cbdfcd6-10c3-4b30-a77c-ebac8c8bf48f.jpg?v=638333788141230000InStockMXN99999DIEbook20239781804614907_W3siaWQiOiJmOTI3MDk2Mi1kN2FhLTRmMWMtOWVjYy05NDk1N2I0OTI3OTQiLCJsaXN0UHJpY2UiOjczOSwiZGlzY291bnQiOjc0LCJzZWxsaW5nUHJpY2UiOjY2NSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781804614907_<p><strong>Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning</strong></p><p><strong>Purchase of the print or Kindle book includes a free PDF eBook</strong></p><h4>Key Features</h4><ul><li>Extract meaningful information from graph data with Neo4js latest version 5</li><li>Use Graph Algorithms into a regular Machine Learning pipeline in Python</li><li>Learn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.</li></ul><h4>Book Description</h4><p>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.</p><p>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. Youll 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, youll 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, youll be able to integrate graph algorithms into your ML pipeline.</p><p>By the end of this book, youll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.</p><h4>What you will learn</h4><ul><li>Use the Cypher query language to query graph databases such as Neo4j</li><li>Build graph datasets from your own data and public knowledge graphs</li><li>Make graph-specific predictions such as link prediction</li><li>Explore the latest version of Neo4j to build a graph data science pipeline</li><li>Run a scikit-learn prediction algorithm with graph data</li><li>Train a predictive embedding algorithm in GDS and manage the model store</li></ul><h4>Who this book is for</h4><p>If youre 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, youll 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.</p>...(*_*)9781804614907_<p><strong>Unlock the power of your data with Neo4j: the leading graph database for data science and machine learning applications.</strong></p><h4>Key Features</h4><ul><li>Learn how to deal with a graph database</li><li>Extract meaningful information from graph data</li><li>Use Graph Algorithms into a regular Machine Learning pipeline in Python</li></ul><h4>Book Description</h4><p>Neo4j and its Graph Data Science Library is a complete solution to store, query and analyze graph data. Graph databases are getting more popular among developers, which means data scientists are likely to face such databases in their future career. Moreover, graph algorithms are a trending topic which enable extracting context information and improve 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 its Graph Data Science Library. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running. Complete with step-by-step explanations of concepts and practical examples. You will begin by querying Neo4j with Cypher and characterize graph datasets. Youll learn how to run graph algorithms on graph data stored into Neo4j, understand the core principles of the Graph Data Science Library to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you will be able to include graph algorithms into your normal ML pipeline. By the end of this book, you will be able to take advantage of the relationships in your dataset to improve your current model and make other types of prediction.</p><h4>What you will learn</h4><ul><li>Querying graph databases such as Neo4j using the Cypher query language</li><li>Build graph datasets from your own data and public knowledge graphs</li><li>Extract new kind of features thanks by connecting observations</li><li>Make graph-specific predictions such as link prediction</li><li>Build a graph data science pipeline with Neo4j</li></ul><h4>Who This Book Is For</h4><p>Data Scientists and data professionals who have learnt the basics of Neo4j and now want to understand how to build advanced analytics solutions will find this graph data science book useful. Familiarity with the major components of a Data Science project in Python and Neo4J is required.</p><h4>Table of Contents</h4><ol><li>Introducing and Installing Neo4j</li><li>Using existing data to build a Knowledge Graph</li><li>Characterizing a Graph Dataset</li><li>Using Graph Algorithms to Characterize a Graph Dataset</li><li>Visualizing Graph Data</li><li>Building a Machine Learning Model with Graph Features</li><li>Automatically Extracting Features with Graph Embeddings for Machine Learning</li><li>Building a GDS Pipeline for Node Classification Model Training</li><li>Predicting Future Edges</li><li>Writing your custom graph algorithm with the Pregel API</li></ol>...9781804614907_Packt Publishinglibro_electonico_b29d0925-0d8f-3956-bac7-73f152b277a8_9781804614907;9781804614907_9781804614907Estelle ScifoInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-2d84f744-49ff-4dc1-b652-fa40061050aa.epub2023-02-23T00:00:00+00:00Packt Publishing