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One of the striking things about machine learning is the wealth of techniques and algorithms available for modeling. There is seemingly a solution for every class of problem. It is easy to forget that data preparation is just as diverse. Each class of problem has an optimal data structure associated with it. Techniques like cluster analysis, association rules, and anomaly detection—typically called unsupervised learning—are no different. They each have distinct data requirements.
Unsupervised methods are exploratory in nature and don’t generate a propensity score in the same way that supervised learning methods do. How do you take these models and automate them in support of organizational decision making? This course will show you how.
In this full-day course, the instructor will demonstrate a variety of examples starting with the correct data structure for these methods. Alignment between data format and problem definition will be reviewed. Different emphases of the business problem will often imply modifications to the way that the data is prepared. The course will then proceed with the exploration and interpretation of candidate models. 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.
Register today for TDWI's unsupervised machine learning data requirements course.
You Will Learn
- When to apply supervised or unsupervised modeling methods
- The importance of careful problem definition in the selection of a technique
- How to prepare data specifically for unsupervised methods
- Options for inserting unsupervised models into the decision-making process of the organization
- How to creatively combine supervised and unsupervised models for greater performance
- Effective techniques for deploying the results of unsupervised modeling
- How to interpret and monitor your unsupervised models for continual improvement
- Analytics practitioners
- Data scientists
- IT professionals
- Technology planners
- Business analysts
- Analytics project leaders