TigerGraph Certification: Graph Algorithms for Machine Learning
Graph algorithms are essential building blocks for analyzing your connected data and for conducting machine learning to gain deeper insights from that data. Graph algorithms can be used directly as unsupervised learning, or to enrich training sets for supervised learning.
This course examines five different categories of graph algorithms and how they improve the accuracy of machine learning algorithms. In the course, we will examine the following:
- Shortest path algorithms.
- Centrality algorithms.
- Community detection algorithms.
- Similarity algorithms.
- Classification algorithms.
These graph algorithms can be used for a variety of use cases including entity resolution, fraud detection, knowledge graphs, and recommendation engine.
Upon successful completion of the test, a certificate will be emailed to you – you can share with you manager, colleagues, one or more groups on LinkedIn or another professional network.
Test time: 30-45 minutes | Score required: 70% or higher