Machine Learning Using SAS Viya
Master supervised machine learning techniques using SAS Viya's Model Studio for data preparation, model development, and deployment.
Master supervised machine learning techniques using SAS Viya's Model Studio for data preparation, model development, and deployment.
This comprehensive course teaches machine learning implementation using SAS Viya's Model Studio. Students learn the complete analytical lifecycle, from problem understanding to model deployment. The curriculum covers data preprocessing, feature selection, and various supervised learning algorithms including decision trees, neural networks, and support vector machines. Through hands-on exercises and a continuous business case study, participants master the practical aspects of building, comparing, and deploying machine learning models without requiring programming skills.
4.7
(101 ratings)
8,450 already enrolled
Instructors:
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What you'll learn
Build and optimize decision tree and ensemble models
Develop neural network architectures for prediction
Implement support vector machines effectively
Master data preprocessing and feature selection
Evaluate and compare model performance
Deploy and monitor models in production
Skills you'll gain
This course includes:
272 Minutes PreRecorded video
42 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 9 modules in this course
This course provides comprehensive coverage of supervised machine learning using SAS Viya through nine detailed modules. Students progress from foundational concepts through advanced techniques in model development and deployment. The curriculum emphasizes practical application through Model Studio's pipeline flow interface, enabling learners to build, compare, and deploy machine learning models without programming. The course includes extensive hands-on exercises and a continuous business case study focusing on customer churn prediction.
Course Overview
Module 1 · 1 Hours to complete
Getting Started with Machine Learning and SAS Viya
Module 2 · 5 Hours to complete
Data Preprocessing and Algorithm Selection
Module 3 · 5 Hours to complete
Decision Trees and Ensembles of Trees
Module 4 · 7 Hours to complete
Neural Networks
Module 5 · 4 Hours to complete
Support Vector Machines
Module 6 · 3 Hours to complete
Model Assessment and Deployment
Module 7 · 4 Hours to complete
Additional Nodes
Module 8 · 1 Hours to complete
Certification Practice Exam
Module 9 · 1 Hours to complete
Fee Structure
Instructors
Senior Analytical Training Consultant
Jeffrey R. Thompson, PhD, is a Senior Analytical Training Consultant with the Advanced Analytics team in the Education division at SAS Institute.
Director, Analytical Education
Dr. Catherine Truxillo serves as Director of Advanced Analytics Education at SAS, where she leads a global team of statisticians, predictive modelers, and data scientists. Since joining SAS in 2000, she has shaped the company's statistical training programs across multiple industries including insurance, finance, manufacturing, and pharmaceuticals. Her expertise spans advanced statistical methods, from multivariate statistics to machine learning. Through her Coursera course "Machine Learning Using SAS Viya," she helps professionals master supervised machine learning techniques and analytics lifecycle management. Her contributions include authoring numerous SAS training courses covering topics such as generalized linear mixed models, structural equation models, and experimental design. Her leadership in developing analytical training materials has influenced SAS's global education initiatives, making complex statistical concepts accessible to diverse audiences while maintaining rigorous technical standards.
Testimonials
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4.7 course rating
101 ratings
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