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Machine Learning Use Cases with Azure

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Presentation to the Data Science Association, Machine Learning Forum on 11/7/15. For all presenations visit: http://www.datascienceassn.org/content/2015-11-07-data-science-machine-learning-forum

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Machine Learning Use Cases with Azure

  1. 1. Machine Learning Use Cases with Azure ML
  2. 2. Agenda Use Cases Azure ML Studio Create a Model Operationalize Model Getting Started
  3. 3. Why Azure ML ? Start in minutes No hardware or installation Only need a browser Use existing R or Python code One-click publishing Use what you need
  4. 4. Use Cases Data Cleaning and Preprocessing Build ML Models Operationalize your Model Technology Mashups Jupyter Notebooks
  5. 5. OOTB Algoritms ANOMALY DETECTION One-class SVM PCA-based anomaly detection Fast training >100 features, aggressive boundary CLUSTERING K-means TWO-CLASS CLASSIFICATION Two-class decision forest Two-class boosted decision tree Two-class decision jungle Two-class locally deep SVM Two-class SVM Two-class averaged perceptron Two-class logistic regression Two-class Bayes point machine Two-class neural network >100 features, linear model Accuracy, fast training Accuracy, fast training, large memory footprint Accuracy, small memory footprint >100 features Accuracy, long training times Fast training, linear model Fast training, linear model Fast training, linear model Discovering structure Finding unusual data points Predicting values Predicting categories Three or more START Two REGRESSION Ordinal regression Poisson regression Fast forest quantile regression Linear regression Bayesian linear regression Neural network regression Decision forest regression Boosted decision tree regression Data in rank ordered categories Predicting event counts Predicting a distribution Fast training, linear model Linear model, small data sets Accuracy, long training time Accuracy, fast training Accuracy, fast training, large memory footprint MULTI-CLASS CLASSIFICATION Multiclass logistic regression Multiclass neural network Multiclass decision forest Multiclass decision jungle One-v-all multiclass Fast training, linear model Accuracy, long training times Accuracy, fast training Accuracy, small memory footprint Depends on the two-class classifier, see notes below Microsoft Azure Machine Learning: Algorithm Cheat Sheet © 2015 Microsoft Corporation. All rights reserved. Created by the Azure Machine Learning Team Email: AzurePoster@microsoft.com Download this poster: http://aka.ms/MLCheatSheet This cheat sheet helps you choose the best Azure Machine Learning Studio algorithm for your predictive analytics solution. Your decision is driven by both the nature of your data and the question you’re trying to answer.
  6. 6. Custom Algorithms R Execute R - Data Processing & Cleanup Create R Model - Predictions Python Execute Python - Data Processing & Cleanup
  7. 7. Demo: ML Studio
  8. 8. Demo: R Model
  9. 9. Demo: Publish a Service
  10. 10. Getting Started https://studio.azureml.net
  11. 11. Free vs. Standard
  12. 12. Questions ? Contact Info: mchenry19@gmail.com @CAMCHENRY http://cmchenry.com http://www.linkedin.com/in/cmchenry https://plus.google.com/+chrismchenry
  13. 13. Backups
  14. 14. Pricing
  15. 15. Experiments
  16. 16. New Experiment
  17. 17. Regression Experiment
  18. 18. Visualize Data
  19. 19. Score a Model
  20. 20. Evaluate Models
  21. 21. Web Services
  22. 22. Notebooks
  23. 23. Datasets
  24. 24. Trained Models
  25. 25. Settings
  26. 26. R Model
  27. 27. Split Module
  28. 28. R Training Model
  29. 29. R Scoring Model
  30. 30. Python Eval of R Model
  31. 31. R Model Evaluate
  32. 32. R Model ROC Curve
  33. 33. R Model Create Scoring
  34. 34. R Model Scoring Exp
  35. 35. R Model Scoring Results
  36. 36. R Model Publish
  37. 37. R Model Service
  38. 38. R Model Req/Resp
  39. 39. R Model Sample Code
  40. 40. Invoking R Model
  41. 41. Testing a Web Service

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