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 analytic opportunities, they do offer a clear path to deployment. This seminar will show you how association models are built and automated in support of organizational decision-making.
The instructor will explore and interpret candidate models and their applications. You will also observe how a mixture of models including business rules, supervised models, and unsupervised models are used together and acted on in real world situations for problems like insurance and fraud detection.
Target Audience: Analytic Practitioners; Data Scientists; IT & BI Professionals; Technology Planners; Consultants; Business Analysts; Analytic Project Leaders