• Specialization: Intro to Statistical Learning 
  • Instructor: Osita Onyejeweke, Instructor
  • Prior knowledge needed: Intro Statistics and Foundational Math

Learning Outcomes 

  • Apply resampling methods in order to obtain additional information about fitted models.
  • Optimize fitting procedures to improve prediction accuracy and interpretability.
  • Understand the benefits and approach of non-linear models.

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Course Content

The module provides an introductory overview of the course and introduces the course instructor.  

To begin the course, we will explore ridge regression, LASSO, and principal component analysis (PCA). These techniques are widely used for regression and dimensionality reduction tasks in machine learning and statistics. 

In this module, we will turn our attention to generalized linear models (GLMs). GLMs are an extension of traditional linear models that allow for modeling a wide range of response variables that may have non-normal distributions.

In this final module, we will explore general additive models (GAMs) and non-parametric regression. GAMs are an extension of GLMs, allowing for the incorporation of non-linear relationships between predictors and the response variable. Non-parametric regression is a type of regression that does not require any assumptions about the form of the relationship between the response variable and predictor variables. This allows for more flexible modeling of complex relationships that may be difficult to capture using traditional linear regression models.

You will complete a multiple choice exam worth 25% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.

Note: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click View on Coursera button above for the most up-to-date information.