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M20774 Perform Cloud Data Science with Azure Machine Learning

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.

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.


Before attending this course, students must 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
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

Course Outline

Module 1: Introduction to Machine Learning
  • What is machine learning?
  • Introduction to machine learning algorithms
  • Introduction to machine learning languages
  • Sign up for Azure machine learning studio account
  • View a simple experiment from gallery
  • Evaluate an experiment
Module 2: Introduction to Azure Machine Learning
  • Azure machine learning overview
  • Introduction to Azure machine learning studio
  • Developing and hosting Azure machine learning applications
  • Explore the Azure machine learning studio workspace
  • Clone and run a simple experiment
  • Clone an experiment, make some simple changes, and run the experiment
Module 3: Managing Datasets
  • Categorizing your data
  • Importing data to Azure machine learning
  • Exploring and transforming data in Azure machine learning
  • Prepare Azure SQL database
  • Import data
  • Visualize data
  • Summarize data
Module 4: Preparing Data for use with Azure Machine Learning
  • Data pre-processing
  • Handling incomplete datasets
  • Explore some data using Power BI
  • Clean the data
Module 5: Using Feature Engineering and Selection
  • Using feature engineering
  • Using feature selection
  • Prepare datasets
  • Use Join to Merge data
Module 6: Building Azure Machine Learning Models
  • Azure machine learning workflows
  • Scoring and evaluating models
  • Using regression algorithms
  • Using neural networks
  • Using Azure machine learning studio modules for regression
  • Create and run a neural-network based application
Module 7: Using Classification and Clustering with Azure machine learning models
  • Using classification algorithms
  • Clustering techniques
  • Selecting algorithms
  • Using Azure machine learning studio modules for classification
  • Add k-means section to an experiment
  • Add PCA for anomaly detection
  • Evaluate the models
Module 8: Using R and Python with Azure Machine Learning
  • Using R
  • Using Python
  • Incorporating R and Python into Machine Learning experiments
  • Exploring data using R
  • Analyzing data using Python
Module 9: Initializing and Optimizing Machine Learning Models
  • Using hyper-parameters
  • Using multiple algorithms and models
  • Scoring and evaluating Models
  • Using hyper-parameters
Module 10: Using Azure Machine Learning Models
  • Deploying and publishing models
  • Consuming Experiments
  • Deploy machine learning models
  • Consume a published model
Module 11: Using Cognitive Services
  • Cognitive services overview
  • Processing language
  • Processing images and video
  • Recommending products
  • Build a language application
  • Build a face detection application
  • Build a recommendation application
Module 12: Using Machine Learning with HDInsight
  • Introduction to HDInsight
  • HDInsight cluster types
  • HDInsight and machine learning models
  • Provision an HDInsight cluster
  • Use the HDInsight cluster with MapReduce and Spark
Module 13: Using R Services with Machine Learning
  • R and R server overview
  • Using R server with machine learning
  • Using R with SQL Server
  • Deploy DSVM
  • Prepare a sample SQL Server database and configure SQL Server and R
  • Use a remote R session
  • Execute R scripts inside T-SQL statements
Course length
5 days (40 hours)

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18 февраля 2019

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