EDHarvard University · on edX
Data Science: Building Machine Learning Models
Beginner 24 hours English Professional CertificateFREE
Editor's Pick
Hand-picked by our editors as a top option in this category.
What you'll learn
Understand the basics of machine learning
Apply popular ML algorithms
Use cross-validation to avoid overtraining
Apply regularization techniques
Use principal component analysis
Build a movie recommendation system in R
This course includes
24h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
Compare alternatives for Data Science: Building Machine Learning Models
Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
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Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
RI
Rafael Irizarry
edX instructor
— learners— courses — instructor rating
Taught by Rafael Irizarry, Professor of Biostatistics at Harvard University and author of the widely used HarvardX Data Science series and its companion textbook.
Requirements
- R basics and data-analysis grounding (earlier series courses recommended)
Who this course is for
- Aspiring data scientists
- Analysts learning machine learning
- Learners on the HarvardX Data Science track
About this provider
ED
edX
Non-profit online learning platform founded by Harvard and MIT. 45M+ learners worldwide.
4.5 trust score
Frequently asked questions
Yes — you can audit it for free. An optional verified HarvardX certificate is available for about $149.
R. It's part of HarvardX's R-based Data Science series, which makes it a good complement to Python-based ML courses.
It's introductory to ML but assumes the R and data-analysis skills from the earlier courses in the Data Science Professional Certificate.
About eight weeks at 2–4 hours per week, fully self-paced.
It's more statistical and first-principles (why methods work), where DataCamp and Pluralsight lean hands-on and tool-focused. Many learners do both.