In addition to their professional experience, students who attend this training should already have the following technical knowledge:
- Programming experience using R, and familiarity with common R packages.
- Knowledge of common statistical methods and data analysis best practices.
- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Working knowledge of relational databases.
After completing this course, students will be able to:
- Explain machine learning, and how algorithms and languages are used
- Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio
- Upload and explore various types of data to Azure Machine Learning
- Explore and use techniques to prepare datasets ready for use with Azure Machine Learning
- Explore and use feature engineering and selection techniques on datasets that are to be used with Azure Machine Learning
- Explore and use regression algorithms and neural networks with Azure Machine Learning
- Explore and use classification and clustering algorithms with Azure Machine Learning
- Use R and Python with Azure Machine Learning, and choose when to use a particular language
- Explore and use hyperparameters and multiple algorithms and models, and be able to score and evaluate models
- Explore how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models
- Explore and use the Cognitive Services APIs for text and image processing, to create a recommendation application, and describe the use of neural networks with Azure Machine Learning
- Explore and use HDInsight with Azure Machine Learning
- Explore and use R and R Server with Azure Machine Learning, and explain how to deploy and configure SQL Server to support R services