The course starts with an introduction to various aspects of building experiments in AzureML and using MLStudio to create cohesive machine learning workflows.

Each topic looks at different aspects of AzureML as well as introduces different concepts in machine learning such as regression, clustering and classification and when to use each. The course moves onto more advanced topics such as how the R language can be used to enrich AzureML as well as being able to define neural networks and lastly how to integrate into more complex data orchestrations involving other services in Azure.

Learning format:

40% presentation 40% hands-on labs 20% whiteboard design

Who this course is designed for:

• Data professionals
• Data scientists

What You Will Learn:

• Students will be taught basic machine learning processes and how different components and tasks of Azure Machine Learning map to each step of the process.
• Introduce students to data science techniques in Azure will be covered.
• Understand how to import and manipulate data with Azure Machine Learning Studio.
• How to evaluate model performance in Azure Machine Learning Studio.
• Understand the use of key machine learning algorithms in Azure Machine Learning Studio.
• How to leverage external scripts written in R or Python with Azure Machine Learning Studio.
• How to operationalize an Azure Machine Learning Studio model for consumption by an application.

Outline:

• MODULE 1: Introduction to Azure Machine Learning
• MODULE 2: Connecting to Data
• MODULE 3: Data Cleansing
• MODULE 4: Modeling in Azure Machine Learning
• MODULE 5: Operationalizing Azure Machine Learning Models

Per person price for private on-site training. A minimum number of participants are required for private on-site training.