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SAS Visual Data Mining and Machine Learning

Titlul Nivel Formatul cursurilor
SAS® Viya® et intégration Python pour Machine Learning
Dans cette formation, vous apprendrez à faire communiquer l’API Python avec le serveur CAS depuis l’application web Jupyther Notebook. Vous apprendrez à charger en mémoire des données dans un environnement distribué, à analyser ces données et à créer des modèles prédictifs dans CAS en utilisant les fonctionnalités de bases de Python au travers le package SWAT (SAS Wrapper Analytics Transfer). Vous apprendrez aussi à récupérer les résultats sur votre poste client et à utiliser le langage Python pour comparer des modèles.

3 Nivel mediu 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 Nivel mediu Live Web Classroom
Deep Learning Using SAS Software
This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, recurrent networks, and variants of denoising autoencoders. The neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Transfer learning is covered because the emergence of this field has shown promise in deep learning. Lastly, you learn how to customize a SAS deep learning model to research new areas of deep learning.

3 Nivel mediu Live Web Classroom 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 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 Tweedy 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.

The self-study e-learning includes:

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

4 Expert e-Learning
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.

The self-study e-learning includes:

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

4 Expert Live Web Classroom e-Learning