Supervised Machine Learning Pipelines Using SAS Viya

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Beschrijving

This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, de…

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: SAS, Machine learning, Microsoft SQL Server, Big Data en MySQL.

This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.


The e-learning version of this course provides access to SAS Viya for Learners, which enables students to use the software to complete the practices.

Learn how to

  • Apply the analytical life cycle to business need.
  • Incorporate a business-problem-solving approach in daily activities.
  • Prepare and explore data for analytical model development.
  • Create and select features for predictive modeling.
  • Develop a series of supervised learning models based on different techniques such as decision tree, ensemble of trees (forest and gradient boosting), neural networks, and support vector machines.
  • Evaluate and select the best model based on business needs.
  • Deploy and manage analytical models under production.

Who should attend Business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in correlated areas

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