Regression Analysis

Instructor: Di Wu

Intermediate Level • 4 weeks to complete at 10 hours a week • Flexible Schedule

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

Regression Analysis
Data analysis
Statistical Analysis
Predictive Modeling
Exploratory Data Analysis
Machine Learning Methods
Feature Engineering
Supervised Learning
Statistical Modeling
Scikit Learn (Machine Learning Library)

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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.

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

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

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

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

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