MOC On-Demand: 20774-Perform Cloud Data Science with Azure Machine Learning Course Outline
Please note: This course aligns to Microsoft exam 70-774. Exam 70-774 retired 06-30-2019.
*** Note: This is an On-Demand Self Study Class, 5-days of content, 90-days unlimited access, $995 ***
You can take this class at any time; there are no set dates. It covers the same content as the 5-day instructor-led class of the same name. The cost for this MOC On-Demand class is $995. (Applicable State and Local taxes may be added for On-Demand purchases, depending on your location.) Microsoft Enterprise customers paying with Software Assurance Vouchers, see SATV Payment note below.
MOC On-Demand Learner Profiles
MOC On-Demand is a self-study training solution that was designed for two types of learners. First, MOC On-Demand is a great fit for experienced IT professionals who don't need a traditional 5-day class to upgrade their existing skills. They can pick and choose topics to make the most effective use of their time. Second, MOC On-Demand is perfect for highly-motivated individuals who are new to a technology and need to space their learning over a period of weeks or months. These learners can take their time and repeat sections as needed until they master the new concepts.
About MOC On-Demand
Our MOC On-Demand classes are self-study courses with 30 to 40 hours of content. They include hours of videos, hands-on labs using the actual software, and knowledge checks and were created by Microsoft to mirror the content found in the traditional live instructor-led version of this course. Those features are all part of the standard MOC On-Demand training. But don't settle for the standard MOC On-Demand class! Check out the "ONLC Extras" that you get when purchasing this course from us.
ONLC Training Centers bundles in valuable extras with our MOC On-Demand Courses. These items are not available from other training companies.
Courseware After the Course.
Get the digital courseware that is used in the live, instructor-led version of this class. While the MOC On-Demand access goes away after 90 days, you will have access to the "extra" digital courseware for an unlimited period of time.
24/7 Online Support.
You will be able to chat online with a content matter expert while you are taking your MOC On-Demand class. And, with your permission, the expert can even take over your computer to provide with assistance with your labs.
These add-ons are available exclusively by ONLC Training Centers and are offered to you at an additional cost.
Certification Pak, $150.
Interested in obtaining certification? Get a Transcender practice exam and a Microsoft exam voucher at this reduced price.
ILT Listener, $250.
Want to listen in and follow along with a live Instructor-Led Training (ILT) class? We offer this option for individuals on a limited budget who have time during the day to hear a live class in progress. ILT Listeners have access to their online support chat expert during the class but they do not have direct access to the live instructor.
ILT Participant, $ Varies.
You've purchased MOC On-Demand, have gone through the training and decided that you still want a live class. Just pay difference between MOC On-Demand course and and the Instructor-Led Training (ILT) class and you can have a seat in our live class. Get both self-study and live, instructor-led training for the retail price of the instructor-led class alone!
Paying with Software Assurance Training Vouchers (SATV)
For Microsoft Enterprise customers paying with Software Assurance Vouchers, the cost of this class is 5 vouchers--this includes access to the self-study materials, the student workbook, 24/7 access to an online expert, and a corresponding exam voucher, if applicable, upon request.
Do You Still Prefer a Live, Instructor-led Class?
Already know MOC On-Demand is not right for you? We also offer this same course content in a live, instructor-led format. For more details, click on the link below:
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.
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.
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.
At course completion
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
Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
• 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
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
• 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
At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.
• 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
This module provides techniques to prepare datasets 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
This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.
• 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
This module describes how to use regression algorithms and neural networks with Azure machine learning.
• 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
This module describes how to use classification and clustering algorithms with Azure machine learning.
• 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
This module describes how to use R and Python with azure machine learning and choose when to use a particular language.
• 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
This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate 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
This module explores how to provide end users with Azure machine learning services, and how to share data generated from 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
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.
• 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
This module describes how use HDInsight with Azure machine learning.
• 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
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.
• 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
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