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

Kód kurzu: DMML35

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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.

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: E-learning

Dĺžka kurzu: 14 hodín

Jazyk: en

Cena bez DPH: 720 EUR

Registrovať

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 14 hodín

Jazyk: en

Cena bez DPH: 1 200 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie E-learning 14 hodín en 720 EUR Registrovať
Na vyžiadanie Na vyžiadanie 14 hodín en 1 200 EUR Registrovať
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Kontakt

Cieľová skupina

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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

Štruktúra kurzu

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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

Predpokladané znalosti

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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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