R Classification & Clustering

RStudio 1.1.4    |    Intermediate
  • 8 Videos | 38m 42s
  • Includes Assessment
  • Earns a Badge
Likes 66 Likes 66
Explore the advantages of the programming language R in this 8-video Skillsoft Aspire course. An essential skill for statistical computing and graphics, R is the tool of choice for data science professionals in every industry and field. It both creates reproducible high-quality analyses, and offers unparalleled graphic and charting capabilities. Learners will examine how to apply classification and clustering methods to data science problems by using R. Key concepts covered in this course include performing the preparatory steps needed to create a classification and decision tree; using the rpart library and ctree library to build a decision tree; and how to perform the preparatory steps needed to carry out clustering. Next, explore use of the k-means clustering method; using hierarchical clustering with the hclust and cutree methods; and applying a decision tree method to a classification problem. Finally, learn to train a decision tree classifier by using the data and a relationship inside of those data.

WHAT YOU WILL LEARN

  • perform the preparatory steps needed to create a classification and decision tree
    use the rpart library to build a decision tree
    use the ctree library to build a decision tree
    perform the preparatory steps needed to carry out clustering
  • use the k-means clustering method
    use hierarchical clustering with the hclust and cutree methods
    apply a decision tree method to a classification problem

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 34s
    UP NEXT
  • Playable
    2. 
    Preparing Data for Classification
    6m 28s
  • Locked
    3. 
    Using rpart
    5m 34s
    FREE ACCESS
  • Locked
    4. 
    Using ctree
    6m 10s
    FREE ACCESS
  • Locked
    5. 
    Preparing Data for Clustering
    4m 4s
    FREE ACCESS
  • Locked
    6. 
    Using K-Means Clustering
    3m 49s
    FREE ACCESS
  • Locked
    7. 
    Using Hierarchical Clustering
    5m 20s
    FREE ACCESS
  • Locked
    8. 
    Exercise: Creating a Decision Tree
    5m 43s
    FREE ACCESS

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