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Machine Learning in the Tidyverse

4.0+
13 reviews
Intermediate

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.

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.
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In the following Tracks

Intermediate Tidyverse Toolbox

Go To Track

Machine Learning Scientist in R

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Supervised Machine Learning in R

Go To Track
  1. 1

    Foundations of "tidy" Machine learning

    Free

    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.

    Play Chapter Now
    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
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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 Track

Machine Learning Scientist in R

Go To Track

Supervised Machine Learning in R

Go To Track

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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Don’t just take our word for it

*4.0
from 13 reviews
54%
23%
8%
8%
8%
  • Nicolas F.
    over 1 year

    This was a game changer for me learning how to use machine learning workflows in the tidyverse.

  • Kyaw A.
    over 1 year

    I never thought that I can use Tidyverse package for Machine Learning. Awesome!!! Check it out.

  • Harri T.
    almost 2 years

    An excellent course to prepare for data science certification.

  • Edwin A.
    almost 2 years

    I recommend this course for those who want to learn machine learning using Tidyverse in R.

  • Fabrizio U.
    about 2 years

    One of the best courses

"This was a game changer for me learning how to use machine learning workflows in the tidyverse."

Nicolas F.

"I never thought that I can use Tidyverse package for Machine Learning. Awesome!!! Check it out."

Kyaw A.

"An excellent course to prepare for data science certification."

Harri T.

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