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Supervised learning techniques such as regression, decision trees, and neural networks provide powerful predictive insights. Applied properly, these data-driven insights expose the forces that shape your organization’s outcomes. Models built using these techniques can produce key indicators that optimize the allocation of organizational resources. This course provides the best practices that guide supervised machine learning processes, covering decision trees and regression models in detail.
New users of these established techniques are often impressed with how easy they seem to be. Modeling software to build these models is widely available. However, proper data preparation is necessary, and a systematic approach to model development is required. When projects fail, many incorrectly conclude that the data was not capable of better performance. This one-day course will empower you to avoid these pitfalls. Keith McCormick will teach you strategies for the successful use of supervised machine learning and best practices to follow when building models. Keith will also provide an in-depth treatment of the most important algorithms to learn first: decision trees and regression.
Deployment considerations are also crucial for effective supervised learning. These factors include proper model interpretation and identification of clues that signify the model will perform well on unseen data. Although the predictive power of these 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. The instructor will show how to interpret supervised models with an eye toward decision automation. This course will demonstrate how real-world projects often combine different kinds of supervised models.
You Will Learn
- When to apply supervised modeling methods
- Options for inserting machine learning into your organization’s decision-making
- How to use multiple models for value estimation and classification
- How to properly prepare data for different kinds of supervised models
- How to interpret model coefficients and output to translate across platforms and languages
- How to explore the pros and cons of “black box” models
- How data preparation must be automated in parallel with the model if deployment is to succeed
- How to compare model accuracy scores to model propensity scores that drive decisions at deployment
- Analytics practitioners
- Data scientists
- Machine learning engineers
- IT professionals
- Technology planners
- Business analysts
- Analytics project leaders