University of Colorado Boulder

Regression Analysis

This course is part of Data Analysis with Python Specialization

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

40 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the principles and significance of regression analysis in supervised learning.

  • Implement cross-validation methods to assess model performance and optimize hyperparameters.

  • Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

Details to know

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Assessments

5 quizzes, 1 assignment

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

40 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the Data Analysis with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 6 modules in this course

This week provides an introduction to regression analysis as a powerful supervised learning method. You will delve into the concepts of linear regression, understanding its principles, assumptions, and practical applications.

What's included

1 video4 readings1 quiz1 discussion prompt

This week you will explore polynomial regression, an advanced technique used to capture nonlinear relationships between variables.

What's included

1 video2 readings1 quiz1 discussion prompt

This week focuses on regularization techniques, including Ridge, Lasso, and Elastic Net, which help prevent overfitting and improve the generalization of regression models.

What's included

1 video3 readings1 quiz1 discussion prompt

Throughout this week, you will explore evaluation metrics and cross-validation techniques to assess and optimize regression model performance.

What's included

1 video3 readings1 quiz1 discussion prompt

This week explores ensemble methods in regression analysis, including bagging and boosting, to combine multiple models for improved prediction accuracy.

What's included

1 video3 readings1 quiz1 discussion prompt

The final week focuses on a comprehensive case study where you will apply regression analysis to solve a real-world problem.

What's included

2 readings1 assignment1 discussion prompt

Instructor

Di Wu
University of Colorado Boulder
15 Courses29,007 learners

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