Techniques like cluster analysis, association rules, and anomaly detection are typically called unsupervised learning because they do not require historical outcome data. While these methods open powerful analytics opportunities, they do not come with a clear path to deployment. They are exploratory in nature and don't generate a propensity score in the same way that supervised learning methods do. So how do you take these association models and automate them in support of organizational decision-making? This course will show you how.
This course will demonstrate a variety of examples starting with the exploration and interpretation of candidate models and their applications. Options for acting on results will be explored. You will also observe how a mixture of models including business rules, supervised models, and unsupervised models are used together in real-world situations for various problems like insurance and fraud detection.
You Will Learn
- When to apply supervised versus unsupervised modeling methods
- Effective techniques for deploying the results of unsupervised learning
- Interpret and monitor your unsupervised models for continual improvement
- Options for inserting unsupervised models into the decision-making process of the organization
- How to creatively combine supervised and unsupervised models for greater performance
- Analytics practitioners; data scientists; IT professionals; technology planners; consultants; business analysts; analytics project leaders