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No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
No BI without Machine Learning
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No BI without Machine Learning

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slides presented in MTI-820 (BI) graduate course (ETS) as an invited speaker

slides presented in MTI-820 (BI) graduate course (ETS) as an invited speaker

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  • 1. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions? No BI without Machine Learning Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ 10 March 2011 MTI-820 ETSFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 2. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions?To Much DataFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 3. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions? Why a talk about machine learning and BI? Machine Learning 101 Supervised Learning (classification) Unsupervised Learning (clustering) Training and Testing Important Concepts Let’s dive into practical example Target Marketing Customer behavior Retention Risk Analysis Monitoring Root Cause Analysis - QMonitor Monitoring Root Cause Analysis - QMiner Conclusion Questions?Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 4. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions?Why a talk about machine learning and BI? Machine Learning ⇒ Data-Mining ⇒ BI Prediction or Clutering ⇒ Patterns ⇒ Patterns (revenus $$ ⇑)Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 5. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions?Speaker: Francis Pieraut, P.Eng. M.Sc.A. Master@LISA - Statistical Machine Learning - udm (flayers: C++ Neural Networks lib) Industry - 7 years in Machine Learning/AI startups (mlboost: Python Machine Learning Boost lib) Founder QMiningFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 6. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?AI and Machine Learning - Data-miningFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 7. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Machine Learning and Data-Mining Machine Learning: learn from data Data-mining: extracting patterns from data Machine Learning use extracted patterns to do predictionFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 8. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Machine Learning Learning from data Classification vs Clustering Applications: Attrition, Rank Customer (approve loans and credit card),Fraud detection, Target-Marketing, Risk Analysis (insurance) etc.Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 9. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Supervised Learning (need class tag for each example)Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 10. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Unsupervised Learning - dimension reduction/clusteringFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 11. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Learning ProcessFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 12. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Tanks in the desert (black box danger) Using ML requires insights An algo is only goods as its dataFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 13. Outline Why a talk about machine learning and BI? Supervised Learning (classification) Machine Learning 101 Unsupervised Learning (clustering) Let’s dive into practical example Training and Testing Conclusion Important Concepts Questions?Important Concepts Datasets (features + class) Generalization vs Overfitting Classification vs Clustering Features Quality (invariant and informative)Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 14. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerService provider Find most probable interested clients N most likely to buy (sort DESC probability) google mailFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 15. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerCell phone usage Find users cluster (behavior)Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 16. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerService provider Find most probable clients to quitFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 17. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerInsurance Score customer risk of making a claimFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 18. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerQMonitor - Global Server Incident Mining Find incidents on servers Find patterns (network, server, etc.)Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 19. Outline Target Marketing Why a talk about machine learning and BI? Customer behavior Machine Learning 101 Retention Let’s dive into practical example Risk Analysis Conclusion Monitoring Root Cause Analysis - QMonitor Questions? Monitoring Root Cause Analysis - QMinerQMiner - Global User Experience Incident MiningFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 20. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions?What you should remember? No BI without Machine Learning Machine learning algorithms applications ⇑ goal = generalization⇒good prediction (DON’T OVERFIT) 80-90% pre or post-processing + data visualization Python provide amazing integration **QMining is looking for intership students BI for Business User http://www.qlikview.com/Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning
  • 21. Outline Why a talk about machine learning and BI? Machine Learning 101 Let’s dive into practical example Conclusion Questions? Any questions? ... intership 2011 ⇒ francis@qmining.com http://fraka6.blogspot.com/ .. Thanks, Francis Pieraut francis@qmining.comFrancis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning

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