COUniversity of Washington · on Coursera
Machine Learning: Regression
Beginner English Professional CertificateFREE
What you'll learn
Build and interpret linear regression models
Apply ridge and lasso regularisation
Perform feature selection
Understand the bias-variance trade-off
Evaluate regression models properly
Implement the methods through case studies in Python
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
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Instructor
EF
Emily Fox
Coursera instructor
— learners— courses — instructor rating
Emily Fox and Carlos Guestrin are machine-learning researchers at the University of Washington whose lectures are often singled out as some of the clearest in any online ML course. The specialization takes a practical, case-study-driven approach.
Requirements
- Some Python programming
- Basic maths (linear algebra and statistics help)
Who this course is for
- Learners building practical ML skills
- Data scientists strengthening regression fundamentals
- Anyone in the UW ML Specialization
About this provider
CO
Coursera
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4.6 trust score
Frequently asked questions
It's well-reviewed and recommended for project-oriented learners — Fox and Guestrin's lectures are often called some of the best online. Just note some topics (like SVMs and random forests) get limited coverage across the series.
Some Python and basic maths (linear algebra, statistics) make it much smoother. It's hands-on, so you'll be coding the methods.
You can audit the full course free on Coursera. A certificate and graded assignments require a subscription.
It's the regression-focused course, following the foundations course and preceding the classification and clustering courses.
You code — the case studies have you implement and apply regression methods in Python, which is what makes the material stick.