Supervised Learning in R: Classification

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

This introduction to machine-learning is intended for beginners. It covers four of the most well-known classification algorithms. This course will provide a solid understanding of the approach each algorithm takes to learning tasks and the R functions that are required to apply these tools to your own work.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate R

What You Will Learn

This chapter will provide an overview of the technique while illustrating how to apply it to fundraising data

You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work

Course Content

Expand all sections

Module 1: k-Nearest Neighbors (kNN)

Module 2: Naive Bayes

Module 3: Logistic Regression

Module 4: Classification Trees

Course Instructors

Brett Lantz

Data Scientist at the University of Michigan

Brett Lantz is a data scientist at the University of Michigan and the author of Machine Learning with R. After training as a sociologist, Brett has applied his endless thirst for data to projects tha...