Supervised Machine Learning Procedures Using SAS(R) Viya(R) in SAS(R) Studio

Course code: DMML35

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.

The self-study e-learning includes:

Annotatable course notes in PDF format.
Virtual lab time to practice.

720 EUR

871 EUR including VAT

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR

Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR

	Starting date	Type	Course duration	Language	Price without VAT
	Upon request	E-learning	14 hours	en	720 EUR	Register

	Upon request	Upon request	14 hours	en	1 200 EUR	Register
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Target group

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

Course structure

Introduction to SAS Viya, Data Preparation, and Exploration

Introduction to machine learning and SAS Viya.
Supervised machine learning concepts.

Regression

Introduction to regression.
Categorical inputs.
Interactions and polynomials.
Selecting regression effects.
Optimizing regression complexity.
Interpreting regression models.
Adjustments for oversampling.

Decision Tree

Tree-structure models.
Decision tree model essentials.
Ensemble of trees.

Neural Network

Introduction to neural networks.
Neural network modeling essentials.
Network architecture.
Network learning.

Model Assessment

Model assessment and comparison.

Support Vector Machine

Introduction to support vector machines.
Methods of solution.

Bayesian Networks

Introduction.
Network structures.

Factorization Machines

Introduction to factorization machines.

Selected Topics

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.

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