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course

Unsupervised Learning in R

Intermediate

4.6+

Updated 12/2024

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

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RMachine Learning4 hours16 videos49 exercises3,600 XP51,218Statement of Accomplishment

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Course Description

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.

Prerequisites

Introduction to R

1

Unsupervised learning in R

Welcome to the course!

Identify clustering problems

Introduction to k-means clustering

k-means clustering

Results of kmeans()

Visualizing and interpreting results of kmeans()

How k-means works and practical matters

Handling random algorithms

Selecting number of clusters

Introduction to the Pokemon data

Practical matters: working with real data

Review of k-means clustering

2

Hierarchical clustering

Introduction to hierarchical clustering

Hierarchical clustering with results

Selecting number of clusters

Interpreting dendrogram

Cutting the tree

Clustering linkage and practical matters

Linkage methods

Comparing linkage methods

Practical matters: scaling

Comparing kmeans() and hclust()

Review of hierarchical clustering

3

Dimensionality reduction with PCA

Introduction to PCA

PCA using prcomp()

Results of PCA

Additional results of PCA

Visualizing and interpreting PCA results

Interpreting biplots (1)

Interpreting biplots (2)

Variance explained

Visualize variance explained

Practical issues with PCA

Practical issues: scaling

Additional uses of PCA and wrap-up

4

Putting it all together with a case study

Introduction to the case study

Preparing the data

Exploratory data analysis

Performing PCA

Interpreting PCA results

Variance explained

PCA review and next steps

Communicating PCA results

Hierarchical clustering of case data

Results of hierarchical clustering

Selecting number of clusters

k-means clustering and comparing results

Clustering on PCA results

Wrap-up and review

Unsupervised Learning in R

Course
Complete

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Included withPremium or Teams

Don’t just take our word for it

*4.6

from 22 reviews

77%

9%

14%

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Most recent
Top reviews

Niklas D.

7 months

This is one of the best datacamp courses, especially after finishing supervised learning before. It is very clear and concise and was a lot of fun to work through. In no moment, I was overwhelmed with the input and the chunks of information were easy to digest. Great!

João P.

7 months

cool

Li D.

about 1 year

Review content required

Ángel F.

over 1 year

It keeps simple a very long topic. I loved this course and I hoping to take other course from the same teacher

Alvaro S.

over 1 year

Great

"This is one of the best datacamp courses, especially after finishing supervised learning before. It is very clear and concise and was a lot of fun to work through. In no moment, I was overwhelmed with the input and the chunks of information were easy to digest. Great!"

Niklas D.

"cool"

João P.

"Review content required"

Li D.

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