Gain insights into essential machine learning algorithms in R, including CART, random forest, and XGBoost. Discover model tuning and cross-validation to create accurate, robust data science models.

Dockerfile.tar.gz

tidymodels

The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners looking to master industry algorithms in the field of data science.

In this course, you’ll learn about several essential algorithms used in machine learning, including classification and regression trees (CART), random forest, and XGBoost. CART is a decision tree algorithm that’s used for both classification and regression problems. Random forest is an ensemble learning method that uses multiple decision trees to improve the accuracy of predictions. XGBoost, short for Extreme Gradient Boosting, is a powerful algorithm that’s also used for regression and classification problems. You’ll also learn about cross-validation and model tuning, which are essential skills for crafting valuable machine learning models. 

After taking this course, you’ll have the crucial skills to ensure that the machine learning models you create are accurate, robust, and reliable.

Data Science with R: Decision Trees and Random Forests

A data scientist is given a collection of Microsoft Word documents and asked to categorize them based on their unknown contents. Which form of machine learning would be useful?

A data scientist is given a collection of Microsoft Word documents and asked to categorize them based on their unknown contents. Which form of machine learning would be useful?

A data scientist has landed a job at a self-driving vehicle startup. Which form of machine learning will be most useful?

A data scientist has landed a job at a self-driving vehicle startup. Which form of machine learning will be most useful?

A data scientist works for a nonprofit and has been asked to predict which attendees to fundraisers are likely to donate money based on historical donation data. Which form of machine learning would be most useful?

A data scientist works for a nonprofit and has been asked to predict which attendees to fundraisers are likely to donate money based on historical donation data. Which form of machine learning would be most useful?

**(True or False)** When all the label values for a group of observations are the same, the group is 100 percent pure.

(True or False) When all the label values for a group of observations are the same, the group is 100 percent pure.

**(True or False)** A data scientist trains a decision tree from a dataset of 100 observations. The decision tree model is very large, with 100 nodes. The model has overfit the data.

(True or False) A data scientist trains a decision tree from a dataset of 100 observations. The decision tree model is very large, with 100 nodes. The model has overfit the data.

Test what you’ve learned about machine learning concepts.

Welcome to the Course

Supervised Learning

Classification Tree Math

Using Classification Trees in R

Introducing the Bias-Variance Tradeoff

Model Tuning

Model Tuning with tidymodels

Feature Engineering

Regression Trees

The Random Forest Algorithm

Using Random Forests

Gradient Boosting Trees

Continuing Your Journey

Quiz: Machine Learning Concepts