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Machine Learning with caret in R

4.6+
16 reviews
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This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

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4 Hours24 Videos88 Exercises
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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 caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.
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  1. 1

    Regression Models: Fitting and Evaluating Their Performance

    Free

    In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).

    Play Chapter Now
    Welcome to the course
    50 xp
    In-sample RMSE for linear regression
    50 xp
    In-sample RMSE for linear regression on diamonds
    100 xp
    Out-of-sample error measures
    50 xp
    Out-of-sample RMSE for linear regression
    50 xp
    Randomly order the data frame
    100 xp
    Try an 80/20 split
    100 xp
    Predict on test set
    100 xp
    Calculate test set RMSE by hand
    100 xp
    Comparing out-of-sample RMSE to in-sample RMSE
    50 xp
    Cross-validation
    50 xp
    Advantage of cross-validation
    50 xp
    10-fold cross-validation
    100 xp
    5-fold cross-validation
    100 xp
    5 x 5-fold cross-validation
    100 xp
    Making predictions on new data
    100 xp

In the following tracks

Machine Learning Fundamentals in RMachine Learning Scientist with R

Collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon
Zachary Deane-Mayer HeadshotZachary Deane-Mayer

VP, Data Science at DataRobot

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Max Kuhn HeadshotMax 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.
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Don’t just take our word for it

*4.6
from 16 reviews
75%
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  • Chin-Huai S.
    9 months

    I think the course is very concise and informative.

  • Dimitris L.
    10 months

    great course, excellent instructor

  • Danny W.
    11 months

    Caret is an amazing package, all there is to say about it

  • Pedro D.
    about 1 year

    great course

  • Andrew H.
    about 1 year

    Very informative and certainly I will be using caret in the future.

"I think the course is very concise and informative."

Chin-Huai S.

"great course, excellent instructor"

Dimitris L.

"Caret is an amazing package, all there is to say about it"

Danny W.

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