Machine Learning: Regression
Master regression techniques from simple linear models to advanced methods like Ridge and Lasso regression.
Master regression techniques from simple linear models to advanced methods like Ridge and Lasso regression.
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 Machine Learning 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.
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English
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What you'll learn
Implement multiple regression models using gradient descent
Tune model parameters using cross-validation
Apply regularization techniques like Ridge and Lasso
Select features using various methods including greedy algorithms
Assess model performance and handle bias-variance tradeoff
Skills you'll gain
This course includes:
7.1 Hours PreRecorded video
15 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This comprehensive course covers regression techniques in machine learning, starting from simple linear regression and progressing to advanced methods. Students learn to predict continuous values using various approaches including multiple regression, ridge regression, lasso for feature selection, and nearest neighbors methods. The curriculum emphasizes both theoretical understanding and practical implementation, covering model assessment, bias-variance tradeoff, and optimization algorithms that scale to large datasets.
Welcome
Module 1 · 55 Minutes to complete
Simple Linear Regression
Module 2 · 3 Hours to complete
Multiple Regression
Module 3 · 3 Hours to complete
Assessing Performance
Module 4 · 2 Hours to complete
Ridge Regression
Module 5 · 3 Hours to complete
Feature Selection & Lasso
Module 6 · 4 Hours to complete
Nearest Neighbors & Kernel Regression
Module 7 · 2 Hours to complete
Closing Remarks
Module 8 · 33 Minutes to complete
Fee Structure
Instructors
Leader in Machine Learning and Intelligent Applications
Carlos Guestrin is the Amazon Professor of Machine Learning at the University of Washington's Computer Science & Engineering Department. He is also the co-founder and CEO of Dato, Inc., which focuses on facilitating the development of intelligent applications utilizing large-scale machine learning. Prior to his current role, Guestrin served as the Finmeccanica Associate Professor at Carnegie Mellon University and was a senior researcher at Intel Research Lab in Berkeley.
Expert in Machine Learning and Bayesian Modeling
Emily Fox is an assistant professor and the Amazon Professor of Machine Learning in the Statistics Department at the University of Washington. Previously, she was a faculty member in the Wharton Statistics Department at the University of Pennsylvania. Fox has received several prestigious awards, including the Sloan Research Fellowship, a Young Investigator Award from the U.S. Office of Naval Research, and a National Science Foundation CAREER Award.
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4.8 course rating
5,556 ratings
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