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Machine Learning in the Tidyverse

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

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5 Horas15 Videos52 Exercises
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Descrição do Curso

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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Nas seguintes faixas

Caixa de ferramentas intermediária do Tidyverse

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  1. 1

    Foundations of "tidy" Machine learning

    Livre

    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.

    Reproduzir Capítulo Agora
    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
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Nas seguintes faixas

Caixa de ferramentas intermediária do Tidyverse

Ir para a trilha

Cientista de aprendizado de máquina com R

Ir para a trilha

Aprendizado de máquina supervisionado em R

Ir para a trilha

Datasets

GapminderAttrition

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Eunkyung Park
Dmitriy Gorenshteyn HeadshotDmitriy Gorenshteyn

Lead Data Scientist at Memorial Sloan Kettering Cancer Center

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