Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio
9h 4m + Hands-On Practice
Available in:
Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio
DMML : DMML22
This course covers a variety of machine learning techniques that are performed in a scalable and in-memory execution environment. The course provides hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The machine learning techniques include logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, factorization machine, and Bayesian networks.
Learn How To
- Create a SAS Cloud Analytic Services (CAS) session, and prepare and explore data for machine learning.
- Build linear and logistic regression models.
- Build decision tree, forest, and gradient boosting models.
- Build neural network models.
- Build support vector machine models.
- Build factorization machine models.
- Evaluate and compare model results.
- Score selected models.
- Build a Bayesian network model.
Who Should Attend
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
Prerequisites
Before attending this course, you should have, at minimum, an introductory-level familiarity with basic statistics. SAS experience is helpful but not required. Coding experience is helpful but not required.
SAS Products Covered
SAS Viya
Course Outline
Introduction to SAS Viya, Data Preparation, and Exploration
- Introduction to machine learning and SAS Viya.
- Supervised machine learning concepts.
- Introduction to regression.
- Categorical inputs.
- Interactions and polynomials.
- Selecting regression effects.
- Optimizing regression complexity.
- Interpreting regression models.
- Adjustments for oversampling.
- Tree-structure models.
- Decision tree model essentials.
- Ensemble of trees.
- Introduction to neural networks.
- Neural network modeling essentials.
- Network architecture.
- Network learning.
- Model assessment and comparison.
- Introduction to support vector machines.
- Methods of solution.
- Introduction.
- Network structures.
- Introduction to factorization machines.
- Selected topics.
- References.
Live Class Schedule
Duration: 14 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
Live Class Schedule
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Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.