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Course Description

The goal of this course is to introduce you to the fundamental concepts and techniques used in predictive modeling. Throughout this course, you will evaluate the balance between model flexibility and interpretability, examine how to select the best parameters using cross-validation, and practice building models that generalize well to new data. You will also explore techniques for splitting datasets, selecting tuning parameters, and fitting models using loss functions. By the end of the course, you will have a solid understanding of model flexibility, interpretability, and the bias-variance trade-off, equipping you to effectively build and evaluate predictive models.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Nonlinear Regression Models
  • Modeling Interactions Between Predictors

Faculty Author

Sumanta Basu

Benefits to the Learner

  • Explore how models have tuning parameters that control their flexibility
  • Explain the flexibility-interpretability and bias-variance trade-offs
  • Define overfitting and find out why it is a problem for data scientists
  • Choose an optimal model and tuning parameters using cross-validation approaches

Target Audience

  • Current and aspiring data scientists and analysts
  • Business decision makers
  • Marketing analysts
  • Consultants
  • Executives
  • Anyone seeking to gain deeper exposure to data science

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
3 week
Dates
Sep 04, 2024 to Sep 24, 2024
Total Number of Hours
16.0
Course Fee(s)
Contract Fee $100.00
Type
3 week
Dates
Nov 27, 2024 to Dec 17, 2024
Total Number of Hours
16.0
Course Fee(s)
Contract Fee $100.00
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