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Look inside
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 project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
prerequisites
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- Intermediate Python 3.x skills
- Ability to create lambda functions and list comprehensions
- Intermediate Jupyter Notebook skills
- Ability to execute and debug cells
- Experience in visualizing pandas output
- Intermediate Neo4j skills
- Familiarity with the Cypher query language
- Intermediate pandas skills
- Ability to read, write, and query data from csv files
- Basic graph theory
- Familiar with the concept of nodes and edges
- Intermediate machine learning
- Familiar with the concept of embeddings and the k-nearest neighbor algorithm
you will learn
In this liveProject, you’ll learn to apply machine learning techniques to a real-world knowledge graph.
- Run inductive models (TransE, TransR) on a graph using pyKEEN
- Run a transductive model (GraphSAGE) on a graph using Stellargraph
- Create a recommendation system by combining a graph model with k-nearest neighbors
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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Leverage Embeddings project for free
Leverage Embeddings project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Leverage Embeddings project for free
