Supervised learning solves modern analytics challenges and drives informed organizational decisions. Although the predictive power of machine learning models can be very impressive, there is no benefit unless they inform value-focused actions. Models must be deployed in an automated fashion to continually support decision making for residual impact. And while unsupervised methods open powerful analytic opportunities, they do not come with a clear path to deployment. This course will clarify when each approach best fits the business need and show you how to derive value from both approaches.
Regression, decision trees, neural networks – along with many other supervised learning techniques – provide powerful predictive insights when historical outcome data is available. Once built, supervised learning models produce a propensity score which can be used to support or automate decision making throughout the organization. We will explore how these moving parts fit together strategically.
Unsupervised methods like cluster analysis, anomaly detection, and association rules 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 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.
Analytic Practitioners, Data Scientists, IT Professionals, Technology Planners, Consultants, Business Analysts, Analytic Project Leaders.
1. Model Development Introduction
Current Trends in AI, Machine Learning and Predictive Analytics
2. Strategic and Tactical Considerations in Binary Classification
3. Data Preparation for Supervised Models
4. The Tasks of the Model Phase
5. What is Unsupervised Learning?
6. Wrap-up and Next Steps
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