FavoriteLoadingBookmark this content

DataCamp – Unsupervised Learning in R No ratings yet.

Link to Content:
DataCamp Unsupervised Learning in R

Created/Published/Taught by:
Hank Roark
Nick Carchedi
Tom Jeon

Content Found Via:

Free? Partially: Some Free Content, Some Paid

Cost Range:
$0.00 - $29.00

Tags: / / / /
Content Type: /

Difficulty Rating:

No ratings yet.

From DataCamp:

“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.”

This course consists of four chapters:

  1. Unsupervised learning in R
  2. Hierarchical clustering
  3. Dimensionality reduction with PCA
  4. Putting it all together with a case study

Recommended Prerequisites: none specified; some familiarity with R

Go to Content: DataCamp – Unsupervised Learning in R