index.md

layout	root	permalink	editor_options
lesson	.	index.html	markdown wrap 72

This lesson introduces a selection of machine learning techniques for analyzing tabular data, including random forests and gradient boosted trees. No experience in machine learning is necessary, but learners should be familiar with data analysis and visualization in R.

{% comment %} This is a comment in Liquid {% endcomment %}

Prerequisites

This lesson assumes some familiarity with R, including dplyr and ggplot. Learners who have completed an introductory Data Carpentry lesson in R should be able to follow the presentation. For a good refresher on prerequisite material, consider the lessons Data Analysis and Visualization in R for Ecologists or R for Social Scientists, for example. {: .prereq}

For Instructors

If you are teaching this lesson in a workshop, please see the Instructor notes. {: .prereq}

{% include links.md %}