Machine Learning Using SAS Viya
This course discusses the theoretical foundation for techniques associated with supervised machine learning models. A series of demonstrations and practices is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment and deployment. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data.
Who should attendBusiness analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in correlated areas
Before attending this course, participants should have at least an introductory-level familiarity with basic statistics. Previous SAS software experience is helpful but not required.
This course addresses SAS Viya software.
|Title||Duration||Access Period||Language||Fee||Add to Cart|
|Machine Learning Using SAS Viya||14.0 hours||180 days||English||79 USD|
|Machine Learning Using SAS Viya (2020.1) (+ 20 virtual lab hours)||14.0 hours||180 days||English||600 USD|
|Dates||Location||Time||Language||Fee||Add to Cart|
|25-28 MAY 2021||Live Web||12:00 PM-03:30 PM +04||English||1,000 USD|
|06-09 JUL 2021||Live Web||12:00 PM-03:30 PM +04||English||1,000 USD|
|03-06 AUG 2021||Live Web||09:00 PM-12:30 AM +04||English||1,000 USD|
|17-20 AUG 2021||Live Web||12:00 PM-03:30 PM +04||English||1,000 USD|
|28 SEP-01 OCT 2021||Live Web||12:00 PM-03:30 PM +04||English||1,000 USD|
|25-28 OCT 2021||Live Web||09:00 PM-12:30 AM +04||English||1,000 USD|
|09-12 NOV 2021||Live Web||01:00 PM-04:30 PM +04||English||1,000 USD|
|07-10 DEC 2021||Live Web||01:00 PM-04:30 PM +04||English||1,000 USD|