Regression Analysis

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

Di Wu

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)

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Develop Your Specialized Knowledge

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Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

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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.

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.