SAS Visual Data Mining and Machine Learning

Název Úroveň Typ školení
SAS Visual Data Mining and Machine Learning in SAS Viya: Interactive Machine Learning
This course provides a theoretical foundation for SAS Visual Data Mining and Machine Learning, as well as hands-on experience using the tool through the SAS Visual Analytics interface. The course uses an interactive approach to teach you visualization, model assessment, and model deployment while introducing you to a variety of machine learning techniques.

4 Expert Classroom Live Web Classroom e-Learning
SAS Viya and Python Integration for Machine Learning
In this course, you learn to use the Python API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar Python functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native Python syntax to compare models.

3 Středně pokročilý Classroom Live Web Classroom
SAS Viya and R Integration for Machine Learning
In this course, you learn to use the R API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar R functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native R syntax to compare models.

3 Středně pokročilý Classroom Live Web Classroom
Using SAS Viya REST APIs with Python and R
In this course, you learn to use the R and Python APIs to take control of SAS Cloud Analytic Services (CAS) and submit actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models on CAS using familiar open-source functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.

3 Středně pokročilý e-Learning
Deep Learning Using SAS Software
This course introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes. The course also presents practical methods used to enhance training data to produce better models. Lastly, a method for efficiently searching hyperparameters is described.

3 Středně pokročilý Classroom Live Web Classroom e-Learning
Advanced Machine Learning Using SAS Viya
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 is 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.

4 Expert e-Learning
Tree-Based Machine Learning Methods in SAS Viya
Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest models, and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedie gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated for tree-based ensemble models.

The self-study e-learning includes:

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

4 Expert e-Learning