No BI without Machine Learning
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The document outlines a talk about machine learning and business intelligence. It will begin with an introduction on why machine learning is important for BI. It will then provide a Machine Learning 101 section covering supervised learning, unsupervised learning, training and testing, and important concepts. The talk will then dive into practical examples of using machine learning for tasks like target marketing, customer behavior analysis, retention, risk analysis, and root cause monitoring. It will conclude with a question and answer section.
No BI without Machine Learning
- 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 ETS Francis 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 Data Francis 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 QMining Francis 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-mining Francis 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 prediction Francis 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/clustering Francis 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 Process Francis 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 data Francis 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 - QMiner Service provider Find most probable interested clients N most likely to buy (sort DESC probability) google mail Francis 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 - QMiner Cell 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 - QMiner Service provider Find most probable clients to quit Francis 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 - QMiner Insurance Score customer risk of making a claim Francis 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 - QMiner QMonitor - 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 - QMiner QMiner - Global User Experience Incident Mining Francis 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.com Francis Pieraut francis@qmining.com http://fraka6.blogspot.com/ No BI without Machine Learning