Technology: SQL Server
Software Assurance Value:
The main purpose of the course is to give students 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.
After completing this course, students will be able to:
The 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.
Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
Lab : Introduction to machine Learning
After completing this module, students will be able to:
Module 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Lab : Introduction to Azure machine learning
Module 3: Managing Datasets
At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.
Lab : Visualizing Data
Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.
Lab : Preparing data for use with Azure machine learning
Module 5: Using Feature Engineering and Selection
This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.
Lab : Using feature engineering and selection
Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.
Lab : Building Azure machine learning models
Module 7: Using Classification and Clustering with Azure machine learning models
This module describes how to use classification and clustering algorithms with Azure machine learning.
Lab : Using classification and clustering with Azure machine learning models
Module 8: Using R and Python with Azure Machine Learning
This module describes how to use R and Python with azure machine learning and choose when to use a particular language.
Lab : Using R and Python with Azure machine learning
Module 9: Initializing and Optimizing Machine Learning Models
This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.
Lab : Initializing and optimizing machine learning models
Module 10: Using Azure Machine Learning Models
This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.
Lab : Using Azure machine learning models
Module 11: Using Cognitive Services
This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.
Lab : Using Cognitive Services
Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.
Lab : Machine Learning with HDInsight
Module 13: Using R Services with Machine Learning
This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.
Lab : Using R services with machine learning
In addition to their professional experience, students who attend this course should have: