Md. Saif Kabir Asif has completed

# Supervised Learning in R: Classification

4 hours
3,950 XP

## Course Description

This beginner-level introduction to machine learning covers four of the most common classification algorithms. 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.

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1. 1

### k-Nearest Neighbors (kNN)

Free

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.

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Classification with Nearest Neighbors
50 xp
Recognizing a road sign with kNN
100 xp
Thinking like kNN
50 xp
Exploring the traffic sign dataset
100 xp
Classifying a collection of road signs
100 xp
What about the 'k' in kNN?
50 xp
Understanding the impact of 'k'
50 xp
Testing other 'k' values
100 xp
Seeing how the neighbors voted
100 xp
Data preparation for kNN
50 xp
Why normalize data?
50 xp
2. 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. 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. 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.

In the following tracks

Associate Data ScientistMachine Learning FundamentalsMachine Learning Scientist

Collaborators

Prerequisites

Intermediate R
Brett Lantz

Senior Data Scientist at Sony PlayStation

Brett Lantz currently works as a data scientist at Sony PlayStation, is the author of Machine Learning with R, and teaches machine learning at the Global School in Empirical Research Methods summer program. After training as a sociologist, Brett has applied his endless thirst for data to projects that involve understanding and predicting human behavior in fields including epidemiology, higher education fundraising, and most recently, the video gaming industry.
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