Target group
Data analysts, data miners, mathematicians, statisticians, data scientists, citizen data scientists, qualitative experts, and others who want an introduction to supervised machine learning for predictive modeling
Course structure
Introduction to SAS Viya, Data Preparation, and Exploration
- Introduction to machine learning and SAS Viya.
- Supervised machine learning concepts.
Regression
- Introduction to regression.
- Categorical inputs.
- Interactions and polynomials.
- Selecting regression effects.
- Optimizing regression complexity.
- Interpreting regression models.
- Adjustments for oversampling.
Decision Tree
- Tree-structure models.
- Decision tree model essentials.
- Ensemble of trees.
Neural Network
- Introduction to neural networks.
- Neural network modeling essentials.
- Network architecture.
- Network learning.
Model Assessment
- Model assessment and comparison.
Support Vector Machine
- Introduction to support vector machines.
- Methods of solution.
Bayesian Networks
- Introduction.
- Network structures.
Factorization Machines
- Introduction to factorization machines.
