This course is for SAS Viya users who want to work with supervised machine learning models to make better decisions on big data. The training will have 2 main focusses: (1) The preparation of the data to an Analytic Base Table (ABT) and (2) theoretical concepts and the analytical approach of supervised machine learning models.
This training uses different click-and-point tools within SAS Viya. SAS Studio is used to access the data, explore the data, prepare the data; SAS Data Studio to create ABT tables and SAS Model Studio to develop and deploy the model.
This course can help prepare you for the following certification exam(s): SAS Viya 3.4 Supervised Machine Learning Pipelines.

Learn how to

• apply the analytical life cycle to business need
• incorporate a business-problem-solving approach in daily activities
• To use SAS Studio and SAS Data Studio to access the data, prepare data for analysis and, perform data discovery and analysis
• To use SAS Model Studio to create and select features for predictive modeling, develop a series of supervised learning models, evaluate and select the best model based on business needs and, deploy and manage analytical models under production.

Who should attend

Business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in correlated areas

Formats available	Standard Duration
Classroom:	5.0 days

Before attending this course, participants should have at least an introductory-level familiarity with basic statistics.

This course addresses SAS Viya software.

Introduction

• machine learning in business decision making
• essentials of supervised prediction
• introduction to SAS Viya

Data Preparation with SAS Studio and SAS Data Studio

• accessing the data
• manipulating the data
• data exploration
• feature extraction
• input transformations
• •feature selection
• variable clustering
• best practices

Decision Trees and Ensembles of Trees

• introduction
• tree-structure models
• recursive partitioning
• pruning
• ensemble of trees

Neural Networks

• introduction
• network architecture
• learning

Support Vector Machines and Additional Topics

• large-margin linear classifier
• methods of solution
• nonlinear classifier:Kernel Trick
• selecting your algorithm
• additional tools

Model Assessment and Deployment

• model assessment and comparison
• model deployment