Would I have survived the Titanic? Machine Learning in Microsoft Azure
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Machine Learning or Data Science are one of today's hottest buzzwords. The scenarios in which Machine Learning can be applied are diverse and can range from predicting football scores to personalised recommendations in online shops to predictive maintenance in manufacturing. In this session I will present Azure Machine Learning - a service in Microsoft Azure that anyone can use to build predictive models using the provided machine learning algorithms and deploy it as a web service. Here, I will go through an end-to-end workflow in which I will predict the survival chances on the Titanic.
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Would I have survived the Titanic? Machine Learning in Microsoft Azure
- 1. Would I have survived the Titanic? Machine Learning in Microsoft Azure SQL Saturday 409 Olivia Klose Technical Evangelist, Microsoft @oliviaklose | http://oliviaklose.com
- 3. Bronze Sponsor SQLSaturday Rheinland 201513.06.2015
- 4. Silver Sponsor SQLSaturday Rheinland 201513.06.2015
- 5. Gold Sponsor SQLSaturday Rheinland 201513.06.2015
- 6. You rock! SQLSaturday Rheinland 201513.06.2015
- 7. Save the date! 13.06.2015 SQLSaturday Rheinland 2015
- 8. WHAT IS MACHINE LEARNING? 13.06.2015 SQLSaturday Rheinland 2015
- 9. Machine Learning – Where?
- 10. Machine Learning – Where?
- 11. 13.06.2015 SQLSaturday Rheinland 2015
- 12. 13.06.2015 SQLSaturday Rheinland 2015
- 13. 13.06.2015 SQLSaturday Rheinland 2015
- 14. What is Machine Learning? “The goal of machine learning is to program computers to use example data or past experience to solve a given problem.” Introduction to Machine Learning, 2nd Edition, MIT Press
- 15. What is Machine Learning? Methods and Systems that... adapt predict new data optimise an action extract information summarise data
- 16. What is Machine Learning not? Methods and Systems that... do „Garbage-In- Knowledge-Out“ predict without data modelling & feature engineering are always perfect replace business rules
- 17. Machine Learning – Warum? 1. Too complex: When you can’t code it. (e.g. Natural Language Processing, hand writing recognition, Computer Vision,…) 2. Too much: When you can’t scale it. (e.g. Spam & fraud detection, healthcare) 3. Too specialised: When you have to adapt/personalise. (e.g. Amazon, Netflix) 4. Autonomous: When you can’t track it. (e.g. AI gaming, robotics)
- 18. Advanced Analytics Scenarios 13.06.2015 SQLSaturday Rheinland 2015 EXAMPLE SOLUTIONS
- 19. THE MACHINE LEARNING PROCESS 13.06.2015 SQLSaturday Rheinland 2015
- 21. Hm – what? 𝑓 X = y Input Matrix/Table Output Vector/Column
- 22. Hm – what? ℎ X = y Input Matrix/Table Predicted Output Vector/Column Hypothesis
- 23. Data 13.06.2015 SQLSaturday Rheinland 2015 Forecast Temperature Windy Play tennis? Sunny Low Yes Play Sunny High Yes Don't Play Sunny High No Don't Play Cloudy Low Yes Play Cloudy High No Play Cloudy Low No Play Rainy Low Yes Don't Play Rainy Low No Play Sunny Low No ? 𝑓 x = 𝑦 Features / Input: (Forecast, Temperature, Windy) e.g. x = sunny, low, yes Play / Don‘t Play
- 24. Säubern, transformieren, Mathe Forecast Temperature Windy Play tennis? Sunny Very Low Yes Play Sunny High Yes Don't Play Sunny High Kinda Don't Play Cloudy ? Yes One place Fleecy clouds High No Play Cloudy Low No Play Rainy ? Yes Don't Play Rainy Low No Play Sunny Low No ?
- 25. Säubern, transformieren, Mathe 13.06.2015 SQLSaturday Rheinland 2015 [[ 1.000000], [ -1.000000], [ -1.000000], [ 1.000000], [ 1.000000], [ 1.000000], [ -1.000000], [ 1.000000]] Forecast Temperature Windy Play tennis? Sunny Low Yes Play Sunny High Yes Don't Play Sunny High No Don't Play Cloudy Low Yes Play Cloudy High No Play Cloudy Low No Play Rainy Low Yes Don't Play Rainy Low No Play Sunny Low No ?
- 26. Säubern, transformieren, Mathe 13.06.2015 SQLSaturday Rheinland 2015 [[ 1.000000, 0.000000, 1.000000], [ 1.000000, 1.000000, 1.000000], [ 1.000000, 1.000000, -1.000000], [ 2.000000, 0.000000, 1.000000], [ 2.000000, 1.000000, -1.000000], [ 2.000000, 0.000000, -1.000000], [ 3.000000, 0.000000, 1.000000], [ 3.000000, 0.000000, -1.000000]] Forecast Temperature Windy Play tennis? Sunny Low Yes Play Sunny High Yes Don't Play Sunny High No Don't Play Cloudy Low Yes Play Cloudy High No Play Cloudy Low No Play Rainy Low Yes Don't Play Rainy Low No Play Sunny Low No ?
- 27. Modell Bauen Forecast Temperature WindyYes Cloudy Sunny Low Yes Rainy High No No Yes Yes No Forecast Temperature Windy Play tennis? Sunny Low Yes Play Sunny High Yes Don't Play Sunny High No Don't Play Cloudy Low Yes Play Cloudy High No Play Cloudy Low No Play Rainy Low Yes Don't Play Rainy Low No Play Sunny Low No ?
- 28. Vorhersagen Forecast Temperature Windy Play? Sunny Low No ? Forecast Temperature WindyYes Cloudy Sunny Low Yes Rainy High No No Yes Yes No
- 29. Forecast Temperature WindyYes Cloudy Sunny Low Yes Rainy High No No Yes Yes No Vorhersagen Play! Forecast Temperature Windy Play? Sunny Low No ?
- 30. Popular Machine Learning Models Support Vector Machines Neural Networks Decision Trees
- 31. True Label Positive Negative PredictedLabel Positive True positive (TP) False positive (FP) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑝 Negative False negative (FN) True negative (TN) 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑛 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑡𝑝 + 𝑡𝑛 𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛 Is the model any good? Confusion Matrix 13.06.2015 SQLSaturday Rheinland 2015
- 32. True Label Patient is sick. Patient is healthy. PredictedLabel Testpositive Test correctly states that the patient is sick. Test incorrectly states that the patient is sick (although he/she is healthy). 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑝 Testnegative Test incorrectly states that the patient is healthy (although being sick). Test correctly states that the patient is healthy. 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑛 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑡𝑝 + 𝑡𝑛 𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛 Is the model any good? Confusion Matrix 13.06.2015 SQLSaturday Rheinland 2015
- 33. AZURE MACHINE LEARNING 13.06.2015 SQLSaturday Rheinland 2015
- 34. Azure Machine Learning Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world.
- 35. Azure Machine Learning HDInsight SQL Server VM SQL DB Blobs & Tables Cloud Desktop files Excel spreadsheets Others… Lokal ML Studio IDE for ML Web Service M Monetise Storage Account
- 36. 13.06.2015 SQLSaturday Rheinland 2015 DEMO Surviving on the Titanic
- 37. SO DO I NEED TO LEARN MACHINE LEARNING NOW? 13.06.2015 SQLSaturday Rheinland 2015 DEMO Azure ML Marketplace
- 38. WHAT DID WE DO? 13.06.2015 SQLSaturday Rheinland 2015
- 39. 13.06.2015 SQLSaturday Rheinland 2015 http://aka.ms/MLCheatSheet
- 40. Stream Analytics + Machine Learning In limited preview 13.06.2015 SQLSaturday Rheinland 2015 SELECT text, sentiment(text) FROM myStream http://aka.ms/stream-ml
- 42. Blog-Series on Machine Learning http://aka.ms/MLSerie http://aka.ms/AzureML-Ueberblick http://aka.ms/AzureML-resources
- 43. Free Video Series on Azure ML http://aka.ms/AzureML-MVA
- 44. Further Information 13.06.2015 SQLSaturday Rheinland 2015 aka.ms/azurenow Machine Learning Series http://aka.ms/MLserie http://aka.ms/AzureML-Ueberblick http://aka.ms/AzureML-resources Machine Learning Video-Series (MVA) http://aka.ms/AzureML-MVA Machine Learning Studio http://studio.azureml.net Free E-Book http://aka.ms/MLbook oliviaklose.com aka.ms/MLblog @oliviaklose