Machine Learning

Official Documentation

Service Description

Using Machine Learning, forecasts for further data can be made on the basis of existing data sets. Existing data can be divided into training data and control data. Forecast models can be created from pre-built analysis algorithms or analysis algorithms written using the R programming language. These can be trained using the training data. The forecasting accuracy achieved with the models can then be verified using the control data. The models can, in turn, be provided as a Web service. All of this is possible in an integrated graphical development environment where dataflow charts of data and process components can be created and configured by drag & drop.

Getting Started

  1. Hands-On with Azure Machine Learning
    9/30/2016, Mva
  2. Cloud-Based Machine Learning for the Developer
    5/4/2015, Video, 1:13:33
  3. Intro and Overview Azure Machine Learning
    1/27/2017, Video, 0:26:04
  4. Azure ML Hands-on-Lab
    6/1/2016, Lab
  5. Introducing Azure Machine Learning
    6/26/2016, Whitepaper
  6. Azure Data Analytics for Developers
    11/21/2016, Mva
  7. Building Recommendation Systems in Azure
    9/17/2015, Mva
  8. Microsoft Azure Essentials: Azure Machine Learning
    3/11/2016, Ebook
  9. Data Science in the Cloud with Microsoft Azure Machine Learning and R
    3/17/2016, Ebook

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Azure Documentation

1. Overview
     1.1. What's Machine Learning?
     1.2. Team Data Science Process
          1.2.1. Overview
          1.2.2. Lifecycle
          1.2.3. Walkthroughs
     1.3. Machine Learning Studio
          1.3.1. What's the Studio?
          1.3.2. Studio capabilities
          1.3.3. Infographic: ML basics
     1.4. Frequently asked questions
     1.5. What's new?
2. Get Started
     2.1. Create your first experiment
     2.2. Example walkthrough
          2.2.1. Create a predictive solution
          2.2.2. 1: Create a workspace
          2.2.3. 2: Upload data
          2.2.4. 3: Create experiment
          2.2.5. 4: Train and evaluate
          2.2.6. 5: Deploy web service
          2.2.7. 6: Access web service
     2.3. Data Science for Beginners
          2.3.1. 1: Five questions
          2.3.2. 2: Is your data ready?
          2.3.3. 3: Ask the right question
          2.3.4. 4: Predict an answer
          2.3.5. 5: Copy other people's work
     2.4. R quick start
3. How To
     3.1. Set up tools and utilities
          3.1.1. Set up environments
          3.1.2. Set up virtual machines
          3.1.3. Customize Hadoop
          3.1.4. Set up a virtual machine
               3.1.4.1. DS VM overview
               3.1.4.2. How to use the DS VM
               3.1.4.3. Provision the DS VM
               3.1.4.4. Set up Azure VM
               3.1.4.5. Set up SQL VM
               3.1.4.6. Provision Linux VM
               3.1.4.7. Use Linux VM
          3.1.5. Manage a workspace
               3.1.5.1. Create
               3.1.5.2. Manage
               3.1.5.3. Troubleshoot
               3.1.5.4. Deploy using ARM
               3.1.5.5. Create in another region
     3.2. Analyze business needs
          3.2.1. Technical needs
          3.2.2. Identify your scenario
     3.3. Acquire and understand data
          3.3.1. Load data into storage
               3.3.1.1. Overview
               3.3.1.2. Blob storage
               3.3.1.3. Use Storage Explorer
               3.3.1.4. Use AzCopy
               3.3.1.5. Use Python
               3.3.1.6. Use SSIS
               3.3.1.7. Move to a VM
               3.3.1.8. Move to SQL database
               3.3.1.9. Load into hive tables
               3.3.1.10. Load from on-prem SQL
               3.3.1.11. Load fromSQL partition tables
          3.3.2. Import training data
               3.3.2.1. Overview
               3.3.2.2. From a local file
               3.3.2.3. From online sources
               3.3.2.4. From an experiment
               3.3.2.5. Use on-prem SQL
          3.3.3. Explore and visualize data
               3.3.3.1. Prepare data
               3.3.3.2. Explore data
                    3.3.3.2.1. Overview
                    3.3.3.2.2. Use Pandas
                    3.3.3.2.3. Use SQL VM
                    3.3.3.2.4. Use Hive tables
               3.3.3.3. Sample data
                    3.3.3.3.1. Overview
                    3.3.3.3.2. Use blob storage
                    3.3.3.3.3. Use SQL Server
                    3.3.3.3.4. Use Hive tables
               3.3.3.4. Process data
                    3.3.3.4.1. Access with Python
                    3.3.3.4.2. Process blob data
                    3.3.3.4.3. Use Azure Data Lake
                    3.3.3.4.4. Use SQL VM
                    3.3.3.4.5. Use data pipeline
               3.3.3.5. Process data with Spark
                    3.3.3.5.1. Overview
                    3.3.3.5.2. Explore data
                    3.3.3.5.3. Score models
                    3.3.3.5.4. Advanced data exploration
                    3.3.3.5.5. Use Scala and Spark
     3.4. Develop models
          3.4.1. Engineer and select features
               3.4.1.1. Overview
               3.4.1.2. Use Pandas
               3.4.1.3. Use SQL+Python
               3.4.1.4. Use Hive queries
               3.4.1.5. TDSP feature selection
          3.4.2. Create and train models
               3.4.2.1. Experiment lifecycle management
               3.4.2.2. Manage iterations
               3.4.2.3. Use PowerShell to create models
               3.4.2.4. Select algorithms
                    3.4.2.4.1. Choose algorithms
                    3.4.2.4.2. Algorithm cheat sheet
                    3.4.2.4.3. Use linear regression
                    3.4.2.4.4. Use text analytics
               3.4.2.5. Evaluate and interpret results
                    3.4.2.5.1. Evaluate performance
                    3.4.2.5.2. Optimize parameters
                    3.4.2.5.3. Interpret results
                    3.4.2.5.4. Debug
               3.4.2.6. Use R and Python
                    3.4.2.6.1. Execute R scripts
                    3.4.2.6.2. Author custom R modules
                    3.4.2.6.3. Execute Python scripts
     3.5. Operationalize models
          3.5.1. Overview
          3.5.2. Deploy models
               3.5.2.1. Deploy a web service
               3.5.2.2. How it works
               3.5.2.3. Prepare for deployment
               3.5.2.4. Use external data
               3.5.2.5. Deploy in multi-regions
               3.5.2.6. Use web service parameters
               3.5.2.7. Enable logging
          3.5.3. Manage web services
               3.5.3.1. Use Web Services portal
               3.5.3.2. Manage with APIs
               3.5.3.3. Create endpoints
               3.5.3.4. Scaling
          3.5.4. Retrain models
               3.5.4.1. Overview
               3.5.4.2. Retrain programmatically
               3.5.4.3. Retrain a Classic web service
               3.5.4.4. Retrain with PowerShell
               3.5.4.5. Retrain an existing web service
               3.5.4.6. Troubleshoot
          3.5.5. Consume models
               3.5.5.1. Overview
               3.5.5.2. Use Excel
               3.5.5.3. Use Excel add-in
               3.5.5.4. Use web app template
               3.5.5.5. Use Batch Pool
     3.6. Examples
          3.6.1. Sample experiments
          3.6.2. Sample datasets
          3.6.3. Customer churn example
          3.6.4. End-to-end scenarios
               3.6.4.1. Use Hadoop clusters
               3.6.4.2. Use Hadoop with 1TB
               3.6.4.3. Use SQL Server
               3.6.4.4. Use SQL Data Warehouse
4. Reference
     4.1. Code samples
     4.2. PowerShell module (New)
     4.3. PowerShell module (Classic)
     4.4. Algorithm & Module reference
     4.5. REST API reference
     4.6. Web service error codes
5. Related
     5.1. Cortana Intelligence Gallery
          5.1.1. Overview
          5.1.2. Industries
          5.1.3. Solutions
          5.1.4. Experiments
          5.1.5. Jupyter Notebooks
          5.1.6. Competitions
          5.1.7. Competitions FAQ
          5.1.8. Tutorials
          5.1.9. Collections
          5.1.10. Custom Modules
     5.2. Cortana Intelligence Partner Solutions
          5.2.1. Cortana Intelligence publishing guide
          5.2.2. Cortana Intelligence solution evaluation tool
     5.3. Cortana Analytics
          5.3.1. APIs
               5.3.1.1. Anomaly detection
               5.3.1.2. Cognitive Services
               5.3.1.3. Predictive maintenance
                    5.3.1.3.1. Overview
                    5.3.1.3.2. Architecture
                    5.3.1.3.3. Technical guide
               5.3.1.4. Vehicle telemetry
                    5.3.1.4.1. Overview
                    5.3.1.4.2. Playbook
                    5.3.1.4.3. Setup
6. Resources
     6.1. Azure Roadmap
     6.2. Net# Neural Networks Language
     6.3. Pricing
     6.4. Pricing calculator
     6.5. Service updates
     6.6. Blog
     6.7. MSDN forum
     6.8. Stack Overflow
     6.9. Videos
     6.10. Get help from live chat

Online Training Content

Date Title
1/16/2017 Cortana Intelligence Suite End-to-End
1/16/2017 Azure Developer Workshop (Storage, Cognitive, ML, Stream Analytics, Containers, and Docker)
11/21/2016 Azure Data Analytics for Developers
9/30/2016 Hands-On with Azure Machine Learning
7/4/2016 Design and Implement Big Data & Advanced Analytics Solutions
3/21/2016 Building Blocks: Big Data and Machine Learning
11/2/2015 Data Science and Machine Learning Essentials
9/17/2015 Building Recommendation Systems in Azure

Tools

Tool Description

Videos

Date Title Length
7/14/2017 Applying data science and machine learning to enhance the user experience 0:03:55
7/4/2017 Cloud Tech 10 - 3rd July 2017 - Azure Machine Learning, Jenkins, Petya detection and more 0:10:45
6/27/2017 IT Anomaly Insights: Operational Insights Over Telemetry Data 1:02:05
6/26/2017 Opportunity scoring - Machine Learning model on Sales Pipelines 0:45:49
5/20/2017 Sneak Peak: Network Watcher Connectivity and Machine Learning Integration 0:17:56
5/10/2017 Using StorSimple data with services in Azure (Media Services, HDInsights, AzureML, etc.) 0:25:31
5/4/2017 Project Fortis: Accelerating UN humanitarian aid planning and response with GraphQL 0:18:05
5/4/2017 How Brainshark is making salespeople better through artificial intelligence and Microsoft HoloLens 0:20:55
5/4/2017 How Brainshark is making salespeople better through artificial intelligence and Microsoft HoloLens 0:21:28
5/4/2017 Using Visual Studio for Machine Learning 0:20:57

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