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

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