Learn how to perform data pre-processing and feature engineering using the Graph Data Science (GDS) Library of Neo4J in this 39-minute video tutorial. Explore practical techniques for preparing and enhancing data for machine learning applications within a graph database context. Access the accompanying Jupyter notebook on GitHub to follow along and implement the demonstrated concepts in your own projects.
Overview
Syllabus
Neo4J GDS: Machine Learning Feature Engineering #datascience #machinelearning
Taught by
The Machine Learning Engineer
Related Courses
-
Neo4J GDS: Machine Learning Feature Engineering in Spanish
-
Feature Engineering for Machine Learning
-
Neo4J GDS: Community Detection Algorithms
-
Introduction to Graph Databases, Cypher, and Neo4j 4
-
Neo4J GDS: Machine Learning Node Classification Pipelines
-
Neo4J GDS: Similarity Algorithms for Data Science and Machine Learning
