Machine Learning in the Tidyverse
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
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Course Description
Welcome to the tidyverse! In this course, you will continue on your journey to learn the tidyverse and apply your knowledge to machine learning concepts.
This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.
The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.
You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.
By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.
This course is ideal if you’re looking to integrate R's Tidyverse tools into your machine learning workflows.
Evaluating machine learning models
Throughout this course, you will focus on leveraging the tidyverse tools in R to build, explore, and evaluate machine learning models efficiently.The course begins by introducing the List Column Workflow (LCW), a method for managing multiple models within a single dataframe. It also covers using the broom package to tidy up and explore model outputs, making the complex results more interpretable.
Utilizing tidyr and purrr
Work through practical exercises including building and evaluating regression along with classification models. Explore techniques for tuning hyperparameters to optimize model performance.You will use packages like tidyr and purrr to handle complex data manipulations and model evaluations, ensuring a tidy and systematic approach to machine learning.
Gain real-world application
Explore real-world examples through multiple case studies, such as using the gapminder dataset to predict life expectancy with linear models.By the end of the course, you will have a strong foundation in applying Tidyverse principles to machine learning, enabling them to build, tune, and evaluate models efficiently in a tidy and reproducible manner.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Intermediate Tidyverse Toolbox
Go To TrackMachine Learning Scientist in R
Go To TrackSupervised Machine Learning in R
Go To Track- 1
Foundations of "tidy" Machine learning
FreeThis chapter will introduce you to the backbone of machine learning in the tidyverse, the List Column Workflow (LCW). The LCW will empower you to work with many models in one dataframe.
This chapter will also introduce you to the fundamentals of the broom package for exploring your models.Foundations of "tidy" machine learning50 xpNesting your data100 xpUnnesting your data100 xpExplore a nested cell100 xpThe map family of functions50 xpMapping your data100 xpExpecting mapped output100 xpMapping many models100 xpTidy your models with broom50 xpThe three ways to tidy your model50 xpExtracting model statistics tidily100 xpAugmenting your data100 xp - 2
Multiple Models with broom
This chapter leverages the List Column Workflow to build and explore the attributes of 77 models. You will use the tools from the broom package to gain a multidimensional understanding of all of these models.
Exploring coefficients across models50 xpTidy up the coefficients of your models100 xpWhat can we learn about these 77 countries?50 xpEvaluating the fit of many models50 xpGlance at the fit of your models100 xpBest and worst fitting models100 xpVisually inspect the fit of many models50 xpAugment the fitted values of each model100 xpExplore your best and worst fitting models100 xpImprove the fit of your models50 xpBuild better models100 xpPredicting the future50 xp - 3
Build, Tune & Evaluate Regression Models
In this chapter you will learn how to use the List Column Workflow to build, tune and evaluate regression models. You will have the chance to work with two types of models: linear models and random forest models.
Training, test and validation splits50 xpThe test-train split100 xpCross-validation data frames100 xpMeasuring cross-validation performance50 xpBuild cross-validated models100 xpPreparing for evaluation100 xpEvaluate model performance100 xpBuilding and tuning a random forest model50 xpBuild a random forest model100 xpEvaluate a random forest model100 xpFine tune your model100 xpThe best performing parameter100 xpMeasuring the test performance50 xpBuild & evaluate the best model100 xp - 4
Build, Tune & Evaluate Classification Models
In this chapter you will shift gears to build, tune and evaluate classification models.
Logistic regression models50 xpPrepare train-test-validate parts100 xpBuild cross-validated models100 xpEvaluating classification models50 xpPredictions of a single model100 xpPerformance of a single model100 xpPrepare for cross-validated performance100 xpCalculate cross-validated performance100 xpRandom forest for classification50 xpTune random forest models100 xpRandom forest performance100 xpBuild final classification model100 xpMeasure final model performance100 xpWrap-up50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Intermediate Tidyverse Toolbox
Go To TrackMachine Learning Scientist in R
Go To TrackSupervised Machine Learning in R
Go To TrackDmitriy Gorenshteyn
See MoreLead Data Scientist at Memorial Sloan Kettering Cancer Center
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