20774 Perform Cloud Data Science with Azure Machine Learning

Overview

The main purpose of the course is to give participants the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

Who Should Attend

This primary audience for this course is people who wish to analyze and present data by using Azure Machine Learning. The secondary audience is IT professionals, Developers, and information workers who need to support solutions based on Azure machine learning.

Course Objectives

After completing this course, participants will be able to, among other things:

• Explain machine learning, & how algorithms & languages are used
• Describe the purpose of Azure Machine Learning, & list the main features of Azure Machine Learning
  Studio
• Explore & use feature engineering & selection techniques on datasets that are to be used with Azure Machine Learning
• Explore & use regression algorithms & neural networks with Azure Machine Learning
• Explore & use classification & clustering algorithms with Azure Machine Learning
• Use R & Python with Azure Machine Learning, & choose when to use a particular language
• Explore & use hyperparameters, multiple algorithms, models, be able to score & evaluate models
• Explore how to provide end-users with Azure Machine Learning services, & how to share data generated from Azure Machine Learning models
• Explore & use the Cognitive Services APIs for text & image processing, to create a recommendation application, & describe the use of neural networks with Azure Machine Learning
• Explore & use HDInsight with Azure Machine Learning

Course Prerequisites

Before attending this course, participants should have:

• Programming experience using R, and familiarity with common R packages
• Knowledge of common statistical methods and data analysis best practice
• Basic knowledge of the Microsoft Windows operating system and its core functionality
• Working knowledge of relational
  databases