Advanced Machine Learning Using SAS(R) Viya(R)

Kód kurzu: ADML35

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This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning Using SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models are also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio.

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: 21 hodín

Jazyk: en

Cena bez DPH: 1 080 EUR

Registrovať

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 800 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie E-learning 21 hodín en 1 080 EUR Registrovať
Na vyžiadanie Na vyžiadanie 21 hodín en 1 800 EUR Registrovať
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Kontakt

Cieľová skupina

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Advanced machine learning modelers who use Model Studio

Štruktúra kurzu

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Machine Learning Fundamentals

  • Model Studio review.
  • Classifier performance.
  • Ensemble learning.

Feature Engineering

  • Introduction to feature engineering.
  • Principal component analysis.
  • Singular value decomposition.
  • Robust principal component analysis.
  • Autoencoders.
  • Transforming categorical variables.

Clustering of Variables and Observations

  • Variable clustering.
  • Cluster analysis.

Anomaly Detection

  • Introduction to anomaly detection.
  • Support vector data description.
  • Semi-supervised learning.

External Models in Model Studio

  • Importing SAS Enterprise Miner models.
  • Running SAS/STAT or SAS Enterprise Miner models.
  • Running open-source models.

Machine Learning Automation

  • Automation in SAS Viya.
  • Data preprocessing and feature engineering.
  • Modeling.
  • Automated pipeline creation.
  • Pipeline automation using REST API (self-study).

Tips and Tricks with Model Studio

  • Managing metadata.
  • Working with analysis elements.
  • Using the SAS Code node.
  • Interpreting models with extracted features.
  • Scoring unsupervised learning models.

Predpokladané znalosti

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Before attending this course, it is recommended that you have done the following:
  • Completed the Machine Learning Using SAS Viya course.
  • Obtained some experience with creating and managing SAS data sets, which you can gain from the SAS Programming 1: Essentials course.
  • Acquired some experience building statistical models using SAS Visual Data Mining and Machine Learning software.
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