SAS Certified AI & Machine Learning

SAS Certified AI & Machine Learning Professional

This program is divided in three modules. I. Programming for SAS® Viya® : This module leverages the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. This course is not intended for beginning SAS software users. II. Self -Services Data Preparation in SAS® Viya® : These self-service data preparation capabilities include bringing data in from a variety of sources, preparing and cleansing the data to be fit for purpose, analyzing data for better understanding and governance, and sharing the data with others to promote collaboration and operational use. III. Neural Networks Essentials : This program combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application.

Module 1 - Machine Learning Using SAS Viya 3.4

Topics Covered
  • Prepare and explore data for analytical model development
  • Create and select features for predictive modeling
  • Develop a series of supervised learning models based on different techniques such as decision tree, ensemble of trees (forest and gradient boosting), neural networks, and support vector machines.
  • Evaluate and select the best model based on business needs
  • Deploy and manage analytical models under production.

Module 2 - Natural Language Processing & Computer Vision Using SAS Viya 3.4

There are TWO programs in this module.

2A. SAS Visual Text Analytics in SAS Viya
This program explores the five components of Visual Text Analytics: parsing, concept derivation, topic derivation, text categorization, and sentiment analysis. Sophisticated linguistic queries are constructed to satisfy specific information needs.
Topics Covered
  • Interpret term maps
  • Identify key textual topics automatically in your large document collections
  • Create, modify, and enable (or disable) custom concepts and test linguistic rule definitions with validation checks within the same interactive GUI
  • Create custom Boolean rules to categorize documents with respect to a categorical target variable
  • Modify automatically generated Boolean category rules
  • Extract a document-level sentiment score.
2B. Deep Learning Using SAS Software
This program introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes.
Topics Covered
  • Define and understand deep learning
  • Build models using deep learning techniques
  • Apply models to score (inference) new data
  • Modify data for better analysis results
  • Search the hyperparameter space of a deep learning model

Module 3 - Forecasting & Optimization Using SAS Viya 3.4

This course Predict outcomes and find the best results given resource constraints with forecasting and optimization technology. This module also has following TWO programs.
3A. Forecasting Using Model Studio in SAS Viya
This course provides a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya.
Topics Covered
  • Visualize modeling data using attribute variables
  • Refine forecast models to improve forecast accuracy
  • Apply overrides-generated forecasts
  • Generate forecast data sets for deployment
  • Build and share custom pipelines for large-scale forecasting analyses
3B. Optimization Concepts for Data Science and Artificial Intelligence
This course focuses on linear, nonlinear, and mixed-integer linear optimization concepts in SAS Viya. Participants learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays.
Topics Covered
  • Identify and formulate appropriate approaches to solving various linear, mixed-integer linear, and nonlinear optimization problems
  • Create optimization models commonly used in industry
  • Solve optimization problems using the OPTMODEL procedure in SAS
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