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Machine Learning in the Tidyverse

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5 hours
4,300 XP
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Course Description

This course will teach you to leverage the tools in the "tidyverse" to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a "tidy" way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning.
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  1. 1

    Foundations of "tidy" Machine learning


    This 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.

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    Foundations of "tidy" machine learning
    50 xp
    Nesting your data
    100 xp
    Unnesting your data
    100 xp
    Explore a nested cell
    100 xp
    The map family of functions
    50 xp
    Mapping your data
    100 xp
    Expecting mapped output
    100 xp
    Mapping many models
    100 xp
    Tidy your models with broom
    50 xp
    The three ways to tidy your model
    50 xp
    Extracting model statistics tidily
    100 xp
    Augmenting your data
    100 xp

In the following tracks

Intermediate Tidyverse ToolboxMachine Learning ScientistSupervised Machine Learning


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Chester Ismay
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Sumedh Panchadhar
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Eunkyung Park
Dmitriy Gorenshteyn HeadshotDmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

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