Advanced Machine Learning Using SAS(R) Viya(R)
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.
Virtual Training nebo e-Learning?
Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget group
Advanced machine learning modelers who use Model Studio
Course structure
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.
Prerequisites
- 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.
Jak nás hodnotí
V čem jsou naše reference výjimečné? Nejsou to jednorázové akce. K nám se lidé vrací rádi a nezavírají před námi dveře.
Podívejte se na úplný seznam referenčních klientů, kteří na nás nedají dopustit.
Nejste si jisti, zda je tento kurz pro vás?
Zavolejte nám a my vám poradíme.
Jsme vám k dispozici na telefonním čísle +420 222 553 101 vždy od pondělí do pátku: 9:00 - 17:00.
Nemůžete volat? Zkuste nám napsat
Chcete získat dárek k narozeninám?
