Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, we’ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss our missteps and tips to get to measurable improvements.
This Intro to Graph Databases series is a collection of short videos getting folks started on Graph Databases. You'll learn why graph databases are special, how they're an antibiotic for some use cases, how the database is much more intuitive and efficient than RDBMS and how easy it is to get started. You'll also learn how to model your data in a Property Graph, how to architect your Neo4j environment in a polyglot form and more.
In future episodes, you'l learn querying Neo4j, using the Neo4j Browser, importing data, syncing MongoDB with Neo4j and more.