Regression Analysis
Master statistical modeling with Python through comprehensive regression analysis techniques, from linear to ensemble methods.
Master statistical modeling with Python through comprehensive regression analysis techniques, from linear to ensemble methods.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Implement and interpret linear regression models for real-world datasets
Master polynomial regression for nonlinear relationships
Apply regularization techniques to prevent overfitting
Use cross-validation for model evaluation and optimization
Develop ensemble methods for improved prediction accuracy
Solve real-world problems using regression analysis
Skills you'll gain
This course includes:
0.8 Hours PreRecorded video
5 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course covers fundamental to advanced concepts in regression analysis using Python. Students learn various regression techniques from linear to ensemble methods, with a focus on practical implementation. The curriculum includes hands-on experience with cross-validation, regularization, and model evaluation. Through interactive tutorials and case studies, participants develop skills in applying regression analysis to real-world data scenarios, making it ideal for aspiring data analysts and machine learning practitioners.
Introduction to Regression and Linear Regression
Module 1 · 6 Hours to complete
Polynomial Regression
Module 2 · 6 Hours to complete
Regularization
Module 3 · 6 Hours to complete
Evaluation and Cross Validation
Module 4 · 6 Hours to complete
Ensemble Methods
Module 5 · 6 Hours to complete
Case Study
Module 6 · 7 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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