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Unsupervised Learning in R

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

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4 Stunden16 Videos49 Übungen50.753 LernendeTrophyLeistungsnachweis

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Kursbeschreibung

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.
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Associate Data Scientist in R

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Grundlagen des Machine Learning in R

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Machine Learning Scientist mit R

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  1. 1

    Unsupervised learning in R

    Kostenlos

    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!

    Kapitel Jetzt Abspielen
    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. 2

    Hierarchical clustering

    Hierarchical clustering is another popular method for clustering. The goal of this chapter is to go over how it works, how to use it, and how it compares to k-means clustering.

    Kapitel Jetzt Abspielen
    Introduction to hierarchical clustering
    50 xp
    Hierarchical clustering with results
    100 xp
    Selecting number of clusters
    50 xp
    Interpreting dendrogram
    50 xp
    Cutting the tree
    100 xp
    Clustering linkage and practical matters
    50 xp
    Linkage methods
    100 xp
    Comparing linkage methods
    50 xp
    Practical matters: scaling
    100 xp
    Comparing kmeans() and hclust()
    100 xp
    Review of hierarchical clustering
    50 xp
  3. 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.

    Kapitel Jetzt Abspielen
    Introduction to PCA
    50 xp
    PCA using prcomp()
    100 xp
    Results of PCA
    50 xp
    Additional results of PCA
    50 xp
    Visualizing and interpreting PCA results
    50 xp
    Interpreting biplots (1)
    50 xp
    Interpreting biplots (2)
    50 xp
    Variance explained
    100 xp
    Visualize variance explained
    100 xp
    Practical issues with PCA
    50 xp
    Practical issues: scaling
    100 xp
    Additional uses of PCA and wrap-up
    50 xp
  4. 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.

    Kapitel Jetzt Abspielen
    Introduction to the case study
    50 xp
    Preparing the data
    100 xp
    Exploratory data analysis
    50 xp
    Performing PCA
    100 xp
    Interpreting PCA results
    100 xp
    Variance explained
    100 xp
    PCA review and next steps
    50 xp
    Communicating PCA results
    50 xp
    Hierarchical clustering of case data
    100 xp
    Results of hierarchical clustering
    50 xp
    Selecting number of clusters
    100 xp
    k-means clustering and comparing results
    100 xp
    Clustering on PCA results
    100 xp
    Wrap-up and review
    50 xp
Für Unternehmen

Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

In den folgenden Tracks

Zertifizierung verfügbar

Associate Data Scientist in R

Gehe zu Track

Grundlagen des Machine Learning in R

Gehe zu Track

Machine Learning Scientist mit R

Gehe zu Track

Datensätze

Pokemon dataWisconsin breast cancer data

Mitwirkende

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

Voraussetzungen

Introduction to R
Hank Roark HeadshotHank Roark

Senior Data Scientist, Boeing

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