This course is for JMP users who work with data that have many variables. The course demonstrates various ways to examine high-dimensional data in fewer dimensions, as well as patterns that exist in the data.
Methods for unsupervised learning are presented in which relationships between the observations, as well as relationships between the variables, are uncovered. The course also demonstrates various ways of performing supervised learning where the relationships among both the output variables and the input variables are considered. In the course, emphasis is on understanding the results of the analysis and presenting conclusions with graphs.
Duration: 4 half-day sessions
Registration Fee: $400 US
This course is not available at this time. Please check back at a later date.
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