MOC On-Demand Packaged Set 20774A: Perform Cloud Data Science with Azure Machine Learning (180 Day) (Q4P-00331)

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.

Microsoft On Demand
Microsoft Official Courses On-Demand (MOC On-Demand) blend video, text, hands-on labs and knowledge checks to help you build your Microsoft technology skills on your own schedule, at your own pace and in your own place. No need to spend time and money travelling to a classroom location or adhering to classroom hours with a computer and an Internet connection, your Microsoft Official Courses On-Demand come to you, any time.
  • Microsoft On Demand covers the same objectives as the classroom course by the same name
  • After you activate your course, you have 6 months to complete it. You can work on your course at any time throughout the 6-month period after you activate the course.

Microsoft On Demand Packaged Set
Including
  • Microsoft On Demand Course ODX20774A (includes Microsoft Labs Online)
  • Digital courseware Course 20774A

Audience Profile
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.

Prerequisites
In addition to their professional experience, students who attend this course should have:
  • 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.

Course Objectives
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

 
Detaylari Göster
Course Outline
Module 1: Introduction to Machine Learning

This module introduces machine learning and discussed how algorithms and languages are used.Lessons
  • What is machine learning?
  • Introduction to machine learning algorithms
  • Introduction to machine learning languages

Module 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.Lessons
  • Azure machine learning overview
  • Introduction to Azure machine learning studio
  • Developing and hosting Azure machine learning applications

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.Lessons
  • Categorizing your data
  • Importing data to Azure machine learning
  • Exploring and transforming data in Azure machine learning

Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.Lessons
  • Data pre-processing
  • Handling incomplete datasets

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.Lessons
  • Using feature engineering
  • Using feature selection

Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.Lessons
  • Azure machine learning workflows
  • Scoring and evaluating models
  • Using regression algorithms
  • Using neural networks

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.Lessons
  • Using classification algorithms
  • Clustering techniques
  • Selecting algorithms

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.Lessons
  • Using R
  • Using Python
  • Incorporating R and Python into Machine Learning experiments

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.Lessons
  • Using hyper-parameters
  • Using multiple algorithms and models
  • Scoring and evaluating 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.Lessons
  • Deploying and publishing models
  • Consuming Experiments

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.Lessons
  • Cognitive services overview
  • Processing language
  • Processing images and video
  • Recommending products

Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.Lessons
  • Introduction to HDInsight
  • HDInsight cluster types
  • HDInsight and machine learning models

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.Lessons
  • R and R server overview
  • Using R server with machine learning
  • Using R with SQL Server