Machine Learning with R | Machine Learning with caret

Learn how the R and Caret package can help to implement some of the most common tasks of the data science project lifecycle. The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open-source machine-learning algorithms. If you are a data scientist working with R, the caret package (short for Classification And Regression Training) is a must-have tool in your tool belt. The caret package provides capabilities that are ubiquitous in all stages of the data science project lifecycle. Most important of all, Caret provides a common interface for training, tuning, and evaluating more than 200 machine learning algorithms. Not surprisingly, caret is a surefire way to accelerate your velocity as a data scientist!

In this presentation, we will provide an introduction to the caret package. The focus of the presentation will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work.

Attendees will learn how to:

• Create stratified random samples of data useful for training machine learning models.
• Train machine learning models using caret’s common interface.
• Leverage caret’s powerful features for cross-validation and hyperparameter tuning.
• Scale caret via the use of multi-core, parallel training.
• Increase their knowledge of caret’s many features.

R code and accompanying dataset:
https://code.datasciencedojo.com/data...

caret website:
http://topepo.github.io/caret/index.html

Table of Contents:
0:00 – Intro
3:24 – Motivation
5:07 – Expectation setting
9:23 – The data
11:57 – Caret
1:18:46 – Resources

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