Course
Supervised Learning in R: Classification
IntermediateSkill Level
Updated 04/2026RMachine Learning4 hr14 videos55 Exercises3,950 XP100K+Statement of Accomplishment
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Prerequisites
Intermediate R1
k-Nearest Neighbors (kNN)
As the kNN algorithm literally "learns by example" it is a case in point for starting to understand supervised machine learning. This chapter will introduce classification while working through the application of kNN to self-driving vehicle road sign recognition.
2
Naive Bayes
Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.
3
Logistic Regression
Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, this chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.
4
Classification Trees
Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.
Supervised Learning in R: Classification
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FAQs
What classification algorithms does this course teach?
You will learn four algorithms: k-Nearest Neighbors, Naive Bayes, logistic regression, and classification trees. Each chapter focuses on one method with a hands-on application.
Do I need prior machine learning experience to start?
No. This is a beginner-level course requiring only Introduction to R and Intermediate R. It introduces supervised machine learning concepts from scratch.
What practical scenarios are used to teach each algorithm?
You will classify road signs for self-driving vehicles with kNN, predict destination suggestions with Naive Bayes, analyze fundraising data with logistic regression, and simulate loan approvals with trees.
Why does the course use the Lending Club dataset for classification trees?
Lending Club data illustrates how tree-based models provide transparency in decisions like loan approval, where understanding the reasoning behind a prediction is critical.
How long does the course typically take to complete?
It has 4 chapters and 55 exercises with a median completion time of about 3.7 hours. Each chapter focuses on a single algorithm with applied exercises.
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