Perform Cloud Data Science with Azure Machine Learning (180 Day)

Course Details
Code: ODX20774
Tuition (USD): $1,070.00 $802.50 • Self Paced
This course is available in other formats
Instructor-Led Classroom & Virtual
Perform Cloud Data Science with Azure Machine Learning (20774)

The main purpose of the course is to give students the ability toanalyze and present data by using Azure Machine Learning, and to provide anintroduction to the use of machine learning with big data tools such asHDInsight and R Services.You willhave access to all MOC On-Demand content including lab steps, videos,interactions, knowledge checks and assessments for the entire one hundred and eighty(180) days of the course. However, the labs in this course require a MicrosoftAzure subscription. As part of the first lab in the course, an Azure Pass willbe provisioned so you can configure a Microsoft Azure subscription if youchoose not to use an existing subscription. The Azure Pass subscription willlast for up to ninety (90) days upon redemption. So you decide when to redeemthe pass within the one hundred and eighty (180) day period of your MOCOn-Demand access. 
The main purpose of the course is to give students the ability toanalyze and present data by using Azure Machine Learning, and to provide anintroduction to the use of machine learning with big data tools such asHDInsight and R Services.

Skills Gained

After completing this course, students will be able to:

  • Explain machinelearning, and how algorithms and languages are used
  • Describe the purpose of AzureMachine Learning, and list the main features of Azure Machine Learning Studio
  • Upload and explore various types ofdata to Azure Machine Learning
  • Explore and use techniques toprepare datasets ready for use with Azure Machine Learning
  • Explore and use feature engineeringand selection techniques on datasets that are to be used with Azure MachineLearning
  • Explore and use regressionalgorithms and neural networks with Azure Machine Learning
  • Explore and use classification andclustering algorithms with Azure Machine Learning
  • Use R and Python with Azure MachineLearning, and choose when to use a particular language
  • Explore and use hyperparameters andmultiple algorithms and models, and be able to score and evaluate models
  • Explore how to provide end-userswith Azure Machine Learning services, and how to share data generated fromAzure Machine Learning models
  • Explore and use the CognitiveServices APIs for text and image processing, to create a recommendationapplication, and describe the use of neural networks with Azure MachineLearning
  • Explore and use HDInsight with AzureMachine Learning
  • Explore and use R and R Server with Azure MachineLearning, and explain how to deploy and configure SQL Server to support Rservices

Who Can Benefit

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

Prerequisites

Inaddition to their professional experience, students who attend this course shouldhave:

  • Programmingexperience using R, and familiarity with common R packages
  • Knowledgeof common statistical methods and data analysis best practices.
  • Basicknowledge of the Microsoft Windows operating system and its core functionality.
  • Workingknowledge of relational databases.

Course Details

Outline

Module 1: Introduction to Machine LearningThis 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
Lab : Introduction to machine Learning
  • Sign up for Azure machine learning studio account
  • View a simple experiment from gallery
  • Evaluate an experiment
Aftercompleting this module, students will be able to:
  • Describemachine learning
  • Describemachine learning algorithms
  • Describemachine learning languages
Module 2: Introduction to Azure Machine LearningDescribe 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
Lab : Introduction to Azure machine learning
  • Explore the Azure machine learning studio workspace
  • Clone and run a simple experiment
  • Clone an experiment, make some simple changes, and run the experiment
Aftercompleting this module, students will be able to:
  • Describe Azure machine learning.
  • Use the Azure machine learning studio.
  • Describethe Azure machine learning platforms and environments.
Module 3: Managing DatasetsAt 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
Lab : Managing Datasets
  • Prepare Azure SQL database
  • Import data
  • Visualize data
  • Summarize data
Aftercompleting this module, students will be able to:
  • Understand the types of data they have.
  • Upload data from a number of different sources.
  • Explorethe data that has been uploaded.
Module 4: Preparing Data for use with Azure Machine LearningThis module provides techniques to prepare datasets for use with Azure machine learning.
Lessons
  • Data pre-processing
  • Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
  • Explore some data using Power BI
  • Clean the data
Aftercompleting this module, students will be able to:
  • Pre-process data to clean and normalize it.
  • Handle incomplete datasets.
Module 5: Using Feature Engineering and SelectionThis 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
Lab : Using feature engineering and selection
  • Prepare datasets
  • Use Join to Merge data
Aftercompleting this module, students will be able to:
  • Usefeature engineering to manipulate data.
  • Usefeature selection.
Module 6: Building Azure Machine Learning ModelsThis 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
Lab : Building Azure machine learning models
  • Using Azure machine learning studio modules for regression
  • Create and run a neural-network based application
Aftercompleting this module, students will be able to:
  • Describe machine learning workflows.
  • Explain scoring and evaluating models.
  • Describe regression algorithms.
  • Usea neural-network.
Module 7: Using Classification and Clustering with Azure machine learning modelsThis module describes how to use classification and clustering algorithms with Azure machine learning.
Lessons
  • Using classification algorithms
  • Clustering techniques
  • Selecting algorithms
Lab : Using classification and clustering with Azure machine learning models
  • Using Azure machine learning studio modules for classification.
  • Add k-means section to an experiment
  • Add PCA for anomaly detection.
  • Evaluate the models
Aftercompleting this module, students will be able to:
  • Useclassification algorithms.
  • Describeclustering techniques.
  • Selectappropriate algorithms.
Module 8: Using R and Python with Azure Machine LearningThis 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
Lab : Using R and Python with Azure machine learning
  • Exploring data using R
  • Analyzing data using Python
Aftercompleting this module, students will be able to:
  • Explainthe key features and benefits of R.
  • Explainthe key features and benefits of Python.
  • UseJupyter notebooks.
  • SupportR and Python.
Module 9: Initializing and Optimizing Machine Learning ModelsThis 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
Lab : Initializing and optimizing machine learning models
  • Using hyper-parameters
Aftercompleting this module, students will be able to:
  • Usehyper-parameters.
  • Usemultiple algorithms and models to create ensembles.
  • Scoreand evaluate ensembles.
Module 10: Using Azure Machine Learning ModelsThis 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
Lab : Using Azure machine learning models
  • Deploy machine learning models
  • Consume a published model
Aftercompleting this module, students will be able to:
  • Deployand publish models.
  • Exportdata to a variety of targets.
Module 11: Using Cognitive ServicesThis 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
Lab : Using Cognitive Services
  • Build a language application
  • Build a face detection application
  • Build a recommendation application
Aftercompleting this module, students will be able to:
  • Describecognitive services.
  • Processtext through an application.
  • Processimages through an application.
  • Createa recommendation application.
Module 12: Using Machine Learning with HDInsightThis module describes how use HDInsight with Azure machine learning.
Lessons
  • Introduction to HDInsight
  • HDInsight cluster types
  • HDInsight and machine learning models
Lab : Machine Learning with HDInsight
  • Provision an HDInsight cluster
  • Use the HDInsight cluster with MapReduce and Spark
Aftercompleting this module, students will be able to:
  • Describethe features and benefits of HDInsight.
  • Describethe different HDInsight cluster types.
  • UseHDInsight with machine learning models.
Module 13: Using R Services with Machine LearningThis 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
Lab : Using R services with machine learning
  • 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
Aftercompleting this module, students will be able to:
  • Implementinteractive queries.
  • Performexploratory data analysis.