Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Start Course for Free4 Hours24 Videos88 Exercises44,990 Learners
6200 XPCreate Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.Loved by learners at thousands of companies
Course Description
Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular <code>caret</code> R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.
- 1
Regression models: fitting them and evaluating their performance
FreeIn the first chapter of this course, you'll fit regression models withtrain()and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE). - 2
Classification models: fitting them and evaluating their performance
In this chapter, you'll fit classification models withtrain()and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC). - 3
Tuning model parameters to improve performance
In this chapter, you will use thetrain()function to tweak model parameters through cross-validation and grid search. - 4
Preprocessing your data
In this chapter, you will practice usingtrain()to preprocess data before fitting models, improving your ability to making accurate predictions. - 5
Selecting models: a case study in churn prediction
In the final chapter of this course, you'll learn how to useresamples()to compare multiple models and select (or ensemble) the best one(s).
Nick Carchedi
Tom JeonPrerequisites
Introduction to Regression in R
Zachary Deane-Mayer
Automation First Data Scientist at DataRobot
Zach is a Data Scientist at DataRobot and co-author of the caret R package. He's fascinated by predicting the future and spends his free time competing in predictive modeling competitions. He's currently one of top 500 data scientists on Kaggle and took 9th place in the Heritage Health Prize as part of the Analytics Inside team.
Max Kuhn
Software Engineer at RStudio and creator of caret
Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, AppliedPredictiveModeling, Cubist, C50 and SparseLDA. He routinely teaches classes in predictive modeling at Predictive Analytics World and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics, USAA
Join over 7 million learners and start Machine Learning with caret in R today!
Create Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.