MOC On-Demand: 20774-Perform Cloud Data Science with Azure Machine Learning - On-Demand Course

  The Perform Cloud Data Science with Azure Machine Learning - 20774 course is a 5-day course that is designed to teach students how 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.

The term hybrid, in this context, means integrating infrastructure technologies that customers host in on-premises datacenters with Azure IaaS and PaaS services. The course provides an overview of these services, providing the knowledge necessary to design hybrid solutions properly. The course also covers a number of demonstrations and labs which enable students to develop hands-on skills that are necessary when implementing such solutions.

Topics covered in the course include:

• Understand machine learning, and how algorithms and languages are used
• Describing the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio
• Uploading and exploring various types of data to Azure Machine Learning
• Exploring and using techniques to prepare datasets ready for use with Azure Machine Learning
• Exploring and using feature engineering and selection techniques on datasets that are to be used with Azure Machine Learning
• Exploring and using regression algorithms and neural networks with Azure Machine Learning
• Exploring and using classification and clustering algorithms with Azure Machine Learning
• Using R and Python with Azure Machine Learning, and choose when to use a particular language
• Exploring and using hyperparameters and multiple algorithms and models, and be able to score and evaluate models
• Exploring how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models
• Exploring and using 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
• Exploring and using HDInsight with Azure Machine Learning.
• Exploring and using R and R Server with Azure Machine Learning, and explain how to deploy and configure SQL Server to support R services.

Target Student:This course is intended for students 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.

Students should have the following prerequisite experience:

• 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.

Section 1: Introduction to Machine Learning
This section introduces machine learning and discussed how algorithms and languages are used.
Topics :
• 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

Section 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Topics :
• 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

Section 3: Managing Datasets
At the end of this section the student will be able to upload and explore various types of data in Azure machine learning.
Topics :
• 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

Section 4: Preparing Data for use with Azure Machine Learning
This section provides techniques to prepare datasets for use with Azure machine learning.
Topics :
• Data pre-processing
• Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
• Explore some data using Power BI
• Clean the data

Section 5: Using Feature Engineering and Selection
This section describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.
Topics :
• Using feature engineering
• Using feature selection
Lab : Using feature engineering and selection
• Prepare datasets
• Use Join to Merge data

Section 6: Building Azure Machine Learning Models
This section describes how to use regression algorithms and neural networks with Azure machine learning.
Topics :
• 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 sections for regression
• Create and run a neural-network based application

Section 7: Using Classification and Clustering with Azure machine learning models
This section describes how to use classification and clustering algorithms with Azure machine learning.
Topics :
• Using classification algorithms
• Clustering techniques
• Selecting algorithms
Lab : Using classification and clustering with Azure machine learning models
• Using Azure machine learning studio sections for classification.
• Add k-means section to an experiment
• Add PCA for anomaly detection.
• Evaluate the models

Section 8: Using R and Python with Azure Machine Learning
This section describes how to use R and Python with azure machine learning and choose when to use a particular language.
Topics :
• 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

Section 9: Initializing and Optimizing Machine Learning Models
This section describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.
Topics :
• Using hyper-parameters
• Using multiple algorithms and models
• Scoring and evaluating Models
Lab : Initializing and optimizing machine learning models
• Using hyper-parameters

Section 10: Using Azure Machine Learning Models
This section explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.
Topics :
• Deploying and publishing models
• Consuming Experiments
Lab : Using Azure machine learning models
• Deploy machine learning models
• Consume a published model

Section 11: Using Cognitive Services
This section 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.
Topics :
• 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

Section 12: Using Machine Learning with HDInsight
This section describes how use HDInsight with Azure machine learning.
Topics :
• 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

Section 13: Using R Services with Machine Learning
This section 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.
Topics :
• 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