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Skill up in machine learning using Azure ML

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Build smart apps using machine learning techniques in the cloud

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Skill up in machine learning using Azure ML

  1. 1. data make decision drive actions
  2. 2. • Cost knowledge scalable
  3. 3. Positive Negative
  4. 4. Value
  5. 5. DATA Business apps Custom apps Sensors and devices INTELLIGENCE ACTION People Automated Systems
  6. 6. R Python APIs
  7. 7. Get/Prepare Data Build/Edit Experiment Create/Updat e Model Evaluate Model Results Publish Web Service Build ML Model Deploy as Web ServiceProvision Workspace Get Azure Subscription Create Workspace Publish an App Azure Data Marketplace https://studio.azureml.net
  8. 8. Blobs and Tables Hadoop (HDInsight) Relational DB (Azure SQL DB) Data Clients Model is now a web service that is callable Monetize the API through our marketplace API Integrated development environment for Machine Learning ML STUDIO
  9. 9. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Using known data, develop a model to predict unknown data.
  10. 10. Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Documents consist of unstructured text. Machine learning typically assumes a more structured format of examples Process the raw data
  11. 11. Using known data, develop a model to predict unknown data. LabelsDocuments Feature Documents Labels Tech Health Politics Politics Sports Process each data instance to represent it as a feature vector
  12. 12. Known data Data instance i.e. {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
  13. 13. Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Training data Train the Mode l Feature Vectors Base Model Adjust Parameters
  14. 14. Known data with true labels Tech Health Politics Politics Sports Tech Health Politics Politics Sports Tech Health Politics Politics Sports Model’s Performance Difference between “True Labels” and “Predicted Labels” True labels Tech Health Politics Politics Sports Predicte d labels Train the Model Split Detac h +/- +/- +/-
  15. 15. R Python
  16. 16. Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnSupervised
  17. 17. YES|NO numerical value
  18. 18. Classification
  19. 19. Clustering
  20. 20. Regression
  21. 21. https://mva.microsoft.com/ebooks#9780735698178 https://azure.microsoft.com/en- us/documentation/services/machine-learning/ www.edx.org https://github.com/Azure-Readiness/hol-azure-machine-learning/
  22. 22. https://github.com/melzoghbi/DataCamp
  23. 23. http://mostafa.rocks

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