Skip to main content
HomeR

Unsupervised Learning in R

4.6+
22 reviews
Intermediate

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

Start Course for Free
4 hours16 videos49 exercises51,001 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies


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.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Machine Learning Fundamentals in R

Go To Track

Machine Learning Scientist in R

Go To Track
  1. 1

    Unsupervised learning in R

    Free

    The k-means algorithm is one common approach to clustering. Learn how the algorithm works under the hood, implement k-means clustering in R, visualize and interpret the results, and select the number of clusters when it's not known ahead of time. By the end of the chapter, you'll have applied k-means clustering to a fun "real-world" dataset!

    Play Chapter Now
    Welcome to the course!
    50 xp
    Identify clustering problems
    50 xp
    Introduction to k-means clustering
    50 xp
    k-means clustering
    100 xp
    Results of kmeans()
    100 xp
    Visualizing and interpreting results of kmeans()
    100 xp
    How k-means works and practical matters
    50 xp
    Handling random algorithms
    100 xp
    Selecting number of clusters
    100 xp
    Introduction to the Pokemon data
    50 xp
    Practical matters: working with real data
    100 xp
    Review of k-means clustering
    50 xp
  2. 3

    Dimensionality reduction with PCA

    Principal component analysis, or PCA, is a common approach to dimensionality reduction. Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA.

    Play Chapter Now
  3. 4

    Putting it all together with a case study

    The goal of this chapter is to guide you through a complete analysis using the unsupervised learning techniques covered in the first three chapters. You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses.

    Play Chapter Now
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Machine Learning Fundamentals in R

Go To Track

Machine Learning Scientist in R

Go To Track

datasets

Pokemon dataWisconsin breast cancer data

collaborators

Collaborator's avatar
Nick Carchedi
Collaborator's avatar
Tom Jeon

prerequisites

Introduction to R
Hank Roark HeadshotHank Roark

Senior Data Scientist, Boeing

Hank is a Senior Data Scientist at Boeing and a long time user of the R language. Prior to his current role, he led the Customer Data Science team at H2O.ai, a leading provider of machine learning and predictive analytics services.
See More

Don’t just take our word for it

*4.6
from 22 reviews
77%
9%
14%
0%
0%
  • Niklas D.
    6 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.

Join over 15 million learners and start Unsupervised Learning in R today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.