Create a powerful recommendation engine built from an ensemble of graph-based models that will help you tap into New York City’s real estate market by identifying groups of similar properties. You’ll start by working with transductive graph models (TransE and TransR) that are created specifically for knowledge graphs. Transductive learning takes observations from a specific set of training data and applies it to a specific set of test data. Next you’ll build an inductive model (GraphSAGE), which allows for generalized learning on new data (i.e. predictive modeling on previously unseen properties). Lastly, you’ll build the recommender system by using the k-Nearest Neighbor (kNN) algorithm to identify similar properties. When you’re done, you’ll have hands-on experience applying machine learning techniques to real-world knowledge graphs… and possibly a lucrative side hustle.
This liveProject is for data scientists who have a background in graph theory and machine learning and are interested in applying these techniques to a knowledge graph. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to apply machine learning techniques to a real-world knowledge graph.
geekle is based on a wordle clone.