MOC 20774 - Perform Cloud Data Science with Azure Machine Learning
The main purpose of this Perform Cloud Data Science with Azure Machine Learning training class 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.
Microsoft Certified Partner
Webucator is a Microsoft Certified Partner for Learning Solutions (CPLS). This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).
Public Classes: Delivered live online via WebEx and guaranteed to run . Join from anywhere!
Private Classes: Delivered at your offices , or any other location of your choice.
- Learn to explain machine learning, and how algorithms and languages are used.
- Learn to 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.
- Learn to 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 .
- 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
- 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
- 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
- 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
- Using Feature Engineering and Selection
- Using feature engineering
- Using feature selection
- Lab: Using feature engineering and selection
- Prepare datasets
- Use Join to Merge data
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.
Experience in the following is required for this Microsoft Big Data class:
- 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.