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