COJohns Hopkins University · on Coursera
Practical Machine Learning
Beginner English Professional CertificateFREE
What you'll learn
Build predictive models in R with the caret package
Create and use training and test sets
Preprocess data and engineer features
Fit trees, random forests, and boosting models
Evaluate models and avoid overfitting
Apply a practical model-building workflow
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
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Instructor
JL
Jeff Leek
Coursera instructor
— learners— courses — instructor rating
Created by Johns Hopkins biostatistics professors Jeff Leek, Roger Peng, and Brian Caffo, whose Data Science Specialization is one of the most-taken on Coursera. The approach is hands-on and R-centric.
Requirements
- Familiarity with R
- Basic statistics helps
Who this course is for
- R users learning machine learning
- Data analysts in the JHU Data Science track
- Beginners wanting a practical ML overview
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
4.6 trust score
Frequently asked questions
Learners generally see it as one of the stronger courses in the JHU Data Science series and a genuinely practical overview — with the caveat that it's short and doesn't go deep on choosing the right model for a problem.
R — it's built around the caret package. If you specifically want Python ML, a different course (like the UW or DeepLearning.AI ones) is a better fit.
You can audit the full course free on Coursera. A certificate requires a subscription.
Familiarity with R and some basic statistics help. It's practical rather than theory-heavy, so you don't need advanced maths.
It's relatively short, so it teaches the caret workflow well but doesn't give a deep sense of when to use which algorithm — pair it with further study for that.