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Building predictive models in Azure Machine Learning

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This presentation covers how to build and drive insights from data by building machine learning models. The session covers how to develop and train models in Python/R using Azure Machine Learning. The session covers how to explore key concepts in data acquisition, preparation, exploration, and visualization, and take a look at how to build a predictive solution using Azure Machine Learning, R, and Python. The session covers tips and tricks on selecting the right algorithm for your data science problem and how to utilize Machine Learning to solve it.

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Building predictive models in Azure Machine Learning

  1. 1. • Cost knowledge scalable
  2. 2. Positive Negative
  3. 3. Value
  4. 4. DATA Business apps Custom apps Sensors and devices INTELLIGENCE ACTION People Automated Systems
  5. 5. R Python APIs
  6. 6. Fully managed Integrated Flexible Deploy in minutes No software to install, no hardware to manage, all you need is an Azure subscription. Drag, drop and connect interface. Data sources with just a drop down; run across any data. Built-in collection of best of breed algorithms with no coding required. Drop in custom R or use popular CRAN packages. Operationalize models as web services with a single click. Monetize in Machine Learning Marketplace.
  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
  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. 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 ? ? ? ?2020 … … … …… Known data Model Unknown data Weather forecast sample Using known data, develop a model to predict unknown data.
  10. 10. 90°F -26°F 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 Using known data, develop a model to predict unknown data. Predict 2020 Summer
  11. 11. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Using known data, develop a model to predict unknown data.
  12. 12. 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
  13. 13. 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
  14. 14. Known data Data instance i.e. {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
  15. 15. 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
  16. 16. 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 +/- +/- +/-
  17. 17. 1 Problem Framing 2 Get/Prepare Data 3 Develop Model 4 Deploy Model 5 Evaluate / Track Performance 3.1 Analysis/ Metric definition 3.2 Feature Engineering 3.3 Model Training 3.4 Parameter Tuning 3.5 Evaluation
  18. 18. • Supervised learning examples • This customer will like coffee • This network traffic indicates a denial of service attack • Unsupervised learning examples • These customers are similar • This network traffic is unusual
  19. 19. Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnSupervised
  20. 20. YES|NO numerical value
  21. 21. Classification
  22. 22. Clustering
  23. 23. Regression
  24. 24. • Regression problems • Estimate household power consumption • Estimate customer’s income • Classification problems • Power station will|will not meet demand • Customer will respond to advertising
  25. 25. • Binary examples • click prediction • yes|no • over|under • win|loss • Multiclass examples • kind of tree • kind of network attack • type of heart disease
  26. 26. access almost any type of application Azure API Management + AML WS
  27. 27. 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/
  28. 28. https://github.com/melzoghbi/DataCamp
  29. 29. http://mostafa.rocks

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