Hands On Training

Supervised Learning in R: Classification

DataCamp

(3)

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

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

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

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...

Course Reviews

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