AI & Machine Learning Professional

Machine Learning Using SAS^® Viya^®
COURSE 1

This course, which is at the core of the SAS Viya Data Mining and Machine Learning curriculum, teaches you the theoretical foundation for techniques associated with supervised machine learning models. Learn how to analytically approach business problems – and use a business case study to understand each step of the analytical life cycle. This course uses Model Studio, the pipeline flow interface in SAS Viya that helps you prepare, develop, compare and deploy advanced analytics models.

SAS^® Visual Text Analytics in SAS^® Viya^®
COURSE 1

This course, which enables you to use SAS Viya in a distributed computing environment, explores the five components of visual text analytics: parsing, concept derivation, topic derivation, text categorization and sentiment analysis. You’ll parse and analyze documents to find dominant themes and construct linguistic queries to satisfy specific information needs. You’ll also develop an integrated solution using information extracted from subject matter expert rules and machine learning results for model and rule-based topics and categories.

Deep Learning Using SAS^® Software
COURSE 2

Discover the essential components of deep learning, as well as how to build deep feedforward, convolutional and recurrent networks. Use neural networks to solve problems that include traditional classification, image classification and time-dependent outcomes. Explore practical methods used to enhance training data to produce better models, as well as a method for efficiently searching hyperparameters.

Forecasting Using Model Studio in SAS^® Viya^®
COURSE 1

Get a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. Learn how to load data into memory and visualize the time series data to be modeled while introducing and implementing attribute variables. Use pipelines to generate forecasts and select champion pipelines – and discover how to incorporate large-scale forecasting practices. These include the creation of data hierarchies, forecast reconciliation, overrides and forecast model selection best practices.

Optimization Concepts for Data Science and Artificial Intelligence
COURSE 2

This course focuses on linear, nonlinear and mixed-integer linear optimization concepts in SAS Viya. Discover how to formulate optimization problems and make formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce the concepts you've learned.