7 hours (usually 1 day including breaks)
This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
- Naive Bayes
- Multinomial models
- Bayesian categorical data analysis
- Discriminant analysis
- Linear regression
- Logistic regression
- EM Algorithm
- Mixed Models
- Additive Models
- Ridge regression