Training for Your Group
Training On Demand
In this course, you will cover new features and functionality in Windows Server 2012 and Windows Server 2012 R2 including management, networking infrastructure, storage, access control, Hyper-V, high availability, and identity federation. You will also cover Dynamic Access Control (DAC), failover clustering, Microsoft Online Backup and changes with Active Directory, Powershell, Hyper-V, and Active Directory Federation Services (AD FS). This course will update your existing knowledge and skills of previous Windows Server versions to Windows Server 2012, including Windows Server 2012 R2.
Course Length: 5 Days
Audience: Data Professionals who wish to analyze and present data by using Azure Machine Learning. IT Professionals, Developers, and Info Workers who need to support solutions based on Azure Machine Learning.
Prerequisites: In addition to professional experience, students who attend should have programming experience using Rm and familiarity with common R packages, basic knowledge of Windows operating system, and a working knowledge of relational databases.
What You're Going To Learn
- 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.
Register for an Upcoming Date
This Course is Available On Demand
90 Day Access
90 Day Subscription
180 Day Access
180 Day Subscription
180 Day Access +
180 Day Subscription
Module 1: Introduction to Machine Learning
- 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
Module 2: Introduction to Azure Machine Learning
- 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
Module 3: Managing Datasets
- 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
Module 4: Preparing Data for use with Azure Machine Learning
- Data pre-processing
- Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
- Explore some data using Power BI
- Clean the data
Module 5: Using Feature Engineering and Selection
- Using feature engineering
- Using feature selection
Lab : Using feature engineering and 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
Lab : Building Azure machine learning models
- 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
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
Module 8: Using R and Python with Azure Machine Learning
- 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
Module 9: Initializing and Optimizing Machine Learning Models
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating Models
Lab : Initializing and optimizing machine learning models
- Using hyper-parameters
Module 10: Using Azure Machine Learning Models
- Deploying and publishing models
- Consuming Experiments
Lab : Using Azure machine learning models
- Deploy machine learning models
- Consume a published model
Module 11: Using Cognitive Services
- 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
Module 12: Using Machine Learning with HDInsight
- 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
Module 13: Using R Services with Machine Learning
- 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