The Use Cases of Clustering in Oracle Machine Learning
Clustering is an unsupervised machine learning technique that helps find natural groupings within a dataset. Instead of trying to predict a specific outcome, clustering focuses on identifying records that are similar to each other. Data points that share similar...The Use Cases of Association Rules in Oracle Machine Learning
Association rules are a type of unsupervised machine learning technique that helps find relationships between items in a dataset. Instead of predicting a specific outcome, the goal is to spot patterns that show how certain items or events tend to occur together. These...The Use Cases of Anomaly Detection in Oracle Machine Learning
Anomaly detection is an unsupervised machine learning technique that identifies observations which differ significantly from most of the data. Unlike supervised methods such as classification or regression, anomaly detection does not need labelled outcomes. Instead,...The Use Cases of Feature Extraction in Oracle Machine Learning
Previously, we discussed about what feature extraction is and the algorithms that support it. In this post, the focus shifts to why feature extraction is so useful in real world machine learning scenarios. Drawing from Oracle Machine Learning documentation, feature...Oracle Machine Learning (ML) Process Summary
Machine learning projects are most successful when they follow a structured and repeatable process. Oracle Machine Learning adopts a well defined lifecycle that guides projects from the initial business problem through to deployment in a production environment. This...