This course shows how to extend the machine learning techniques of SAS Enterprise Miner into an interactive web-based environment. In the course, you build customized modeling templates to enable sharing of modeling best practices across an organization. In addition, you experiment with Bayesian networks, support vector machines, random forests, and many other machine learning techniques to gain the most insight from your data.
The self-study version of this course contains structured course notes that provide a detailed overview and exercises and that help you develop essential skills. There is also a Virtual Lab that enables you to practice what you learn in the course.
The e-learning includes the following:
- digital course notes for self-study
- Virtual Lab: 20 hours of hands-on software practice
Learn how to
stage a modeling tournament to quickly train and validate models on segmented data.
Who should attend
Predictive modelers, data miners, business analysts, and data scientists
Formats available | Standard duration | | |
Classroom: |
2.0 days | | |
|
Before attending this course, you should be acquainted with Microsoft Windows and Windows-based software. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling, as modeling experience is required. Previous SAS software experience is helpful but not required.
This course addresses SAS Factory Miner software.
