This course covers the skills required to develop, assess, tune, compare, and score predictive models using JMP Pro software.
This course teaches you how to build and understand predictive models using machine learning techniques such as generalized regression models, k-nearest neighbors, naïve Bayes, support vector machines, decision trees, and neural networks. You will also learn how to validate predictive models using cross-validation, holdback validation, and information-theoretic criteria.
Learn how to:
Develop, compare and explain complex models.
Use the partition platform for predictive modeling including bagging, bootstrap forest, boosted trees.
Use neural networks for predictive modeling including k-Fold cross validation, multi-layer neural networks, and boosting.
Tune predictive models.
Score new data in JMP and generate score code for use in other software.