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Cluster Analysis and Ensemble Learning

Cluster Analysis and Ensemble Learning


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
This course covers key concepts related to cluster analysis using Microsoft R and the k-means clustering technique. It also covers ensemble learning for analysis, including random forest analysis.

Target Audience
All individuals who wish to understand key concepts in big data analysis and Microsoft R features including scientists, analysts, and statisticians

Prerequisites
None

Expected Duration (hours)
1.0

Lesson Objectives

Cluster Analysis and Ensemble Learning

  • start the course
  • describe essentials of unsupervised learning
  • recognize main types of clustering analysis techniques
  • define k-means clustering analysis and its use cases
  • define Microsoft R's rxKmeans function and its important arguments used to conduct k-means clustering analysis
  • identify key features of rxKmeans function
  • describe ensemble learning and its key features
  • recognize rxEnsemble function and its important arguments used for ensemble learning
  • define essentials of Random Forests algorithms and Microsoft R's rxFastForest function
  • define essentials of Decision Forests algorithms and Microsoft R's rxFastForest function
  • recognize Microsoft R's rxFastTrees function and its important arguments that implement a gradient-boosting algorithmrecognize Microsoft R's rxFastTrees function and its important arguments that implement a gradient-boosting algorithm
  • recognize Microsoft R's rxBTrees function and its important arguments that implement a stochastic gradient-boosting algorithm
  • define essentials of Neural Networks algorithms and Microsoft R's xNeuralNet algorithm
  • list important model ensembles metrics
  • Course Number:
    df_abdr_a09_it_enus

    Expertise Level
    Intermediate