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.

1 080 EUR

1 307 EUR including VAT

Selection of dates

Do you have a question?

+420 731 175 867 edu@edutrainings.cz

Professional
and certified lecturers

Internationally
recognized certifications

Wide range of technical
and soft skills courses

View soft skills courses

Great customer
service

Making courses
exactly to measure your needs

Course dates
Target group
Course structure
Prerequisites

Course dates

Starting date: Upon request

Type: Virtual

Course duration: 14 hours

Language: en

Price without VAT: 1 080 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		Virtual	14 hours	en	1 080 EUR	Register
G	Guaranteed course

Didn't find a suitable date?

Write to us about listing an alternative tailor-made date.

Contact

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.

Do you need advice or a tailor-made course?

product support

+420 731 175 867

edu@edutrainings.cz

The date:
Takes place:
Form:
Language:
Price without VAT: