The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and Elastic Net), cross-validation, and ensemble methods (bagging, boosting, and stacking). Through interactive tutorials and practical case studies, students will gain hands-on experience in applying regression analysis to real-world data scenarios.
Regression Analysis
This course is part of Data Analysis with Python Specialization
Instructor: Di Wu
Included with
Recommended experience
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.
Skills you'll gain
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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 assignment1 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 assignment1 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 assignment1 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 assignment1 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 assignment1 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
Offered by
Recommended if you're interested in Data Analysis
University of Colorado Boulder
University of Colorado Boulder
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