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Machine Learning Classifiers

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Use Machine learning to solve classification problems through building binary and multi-class classifiers.

Does your company face business-critical decisions that rely on dynamic transactional data? If you answered “yes,” you need to attend this free event featuring Microsoft analytics tools. We’ll focus on Azure Machine Learning capabilities and explore the following topics: - Introduction of two class classification problems.
- Classification Algorithms (Two Class Classification)
- Available algorithms in Azure ML.
- Real business problems that is solved using two class classification.

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Machine Learning Classifiers

  1. 1. Building Machine Learning Classifiers Mostafa Elzoghbi Sr. Technical Evangelist – Microsoft @MostafaElzoghbi http://mostafa.rocks
  2. 2. Session Objectives & Takeaways • What is Machine Learning? • Azure Machine Learning (AML) for ML Solutions • Machine Learning Classifiers • Business Use Cases • Let’s build smart apps initiative!
  3. 3. What is Machine Learning ? • Using known data, develop a model to predict unknown data. Known Data: Big enough archive, previous observations, past data Model: Known data + Algorithms (ML algorithms) Unknown Data: Missing, Unseen, not existing, future data
  4. 4. Microsoft Azure Machine Learning • Web based UI accessible from different browsers • Share|collaborate to any other ML workspace • Drag & Drop visual design|development • Wide range of ML Algorithms catalog • Extend with OSS R|Python scripts • Share|Document with IPython|Jupyter • Deploy|Publish|Scale rapidly (APIs)
  5. 5. Azure Machine Learning Ecosystem Get/Prepare Data Build/Edit Experiment Create/Update 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
  6. 6. 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
  7. 7. DEMOAzure Machine Learning Studio Capabilities
  8. 8. EXAMPLES
  9. 9. Model (Decision Tree) Age<30 Income > $50K Xbox-One Customer Not Xbox-One Customer Days Played > 728 Income > $50K Xbox-One Customer Not Xbox-One Customer Xbox-One Customer
  10. 10. EXAMPLE
  11. 11. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Model (Classification) Using known data, develop a model to predict unknown data.
  12. 12. Known data (Training data) 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. Known data (Training data) 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. Feature vector 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. Developing a Model Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Training data Train the Model Feature Vectors Base Model Adjust Parameters
  16. 16. Model’s Performance 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 Predicted labels Train the Model Split Detach +/- +/- +/-
  17. 17. Steps to Build a Machine Learning Solution 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. Machine Learning Algorithms Flavors of machine learning algorithms • Supervised • Unsupervised • Reinforcement learning (n/a in AML) Most commonly used machine learning algorithms are supervised (requires labels) • 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. Common Classes of Algorithms (Supervised|Unsupervised) Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnsupervised
  20. 20. Classification A classification technique (or classifier) is a systematic approach to build classification models from an input data set. Examples include decision tree classifiers, rule-based classifiers, neural networks, support vector machines, and naıve Bayes classifiers. Scenarios: ▪ Which customer are more likely to buy, stay, leave (churn analysis) ▪ Which transactions|actions are fraudulent ▪ Which quotes are more likely to become orders ▪ Recognition of patterns: speech, speaker, image, movement, etc. Algorithms: Boosted Decision Tree, Decision Forest, Decision Jungle, Logistic Regression, SVM, ANN, etc. (14 algorithms so far) Classification
  21. 21. Binary versus Multiclass Classification Does your customer want a yes|no answer? • Binary examples • click prediction • yes|no • over|under • win|loss • Multiclass examples • kind of tree • kind of network attack • type of heart disease
  22. 22. ML Classifier Types • Two Class Classifiers: • Answer: Yes/No, T/F • Multi-Class Classifiers: • Multiple answers • Definitive list of options
  23. 23. DEMOPredict an individual Income (>=50K) - Binary Classifier
  24. 24. Linear Classifier
  25. 25. Logistic Regression Classifier
  26. 26. Trees, forests, and jungles
  27. 27. Neural networks and perceptrons
  28. 28. Support Vector Machines (SVMs)
  29. 29. Bayesian methods Bayesian methods have a highly desirable quality: they avoid overfitting. They do this by making some assumptions beforehand about the likely distribution of the answer. Another byproduct of this approach is that they have very few parameters. Azure Machine Learning has both Bayesian algorithms for both classification (Two-class Bayes' point machine) and regression (Bayesian linear regression). Note that these assume that the data can be split or fit with a straight line.
  30. 30. Business Use Cases
  31. 31. DEMOAzure Machine Learning Classification Algorithms
  32. 32. DEMO Letter Recognition (Multi-Class Classifier)
  33. 33. References • Free e-book “Azure Machine Learning” • https://mva.microsoft.com/ebooks#9780735698178 • Azure Machine Learning documentation • https://azure.microsoft.com/en-us/documentation/services/machine-learning/ • Data Science and Machine Learning Essentials • www.edx.org • Azure ML Camp Files (labs & presentation) in GitHub: https://github.com/melzoghbi/DataCamp • Azure ML HOL (GitHub): • https://github.com/Azure-Readiness/hol-azure-machine-learning/
  34. 34. Thank you • Check out my blog for Azure ML articles: http://mostafa.rocks • Follow me on Twitter: @MostafaElzoghbi • Want some help in building ML Solutions? Contact me to know more.

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