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OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
No BI without Machine Learning
Francis [email protected]
http://fraka6.blogspot.com/
10 March 2011MTI-820 ETS
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Too Much Data
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Why a talk about machine learning and BI?
Machine Learning 101Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Let’s dive into practical exampleTarget MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
Conclusion
Questions?
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Why a talk about machine learning and BI?
I Machine Learning ⇒ Data-Mining ⇒ BII Prediction or Clutering ⇒ Patterns ⇒ Patterns (revenus $$ ⇑)
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Speaker: Francis Pieraut, P.Eng. M.Sc.A.
I Master@LISA - Statistical Machine Learning - udm(flayers: C++ Neural Networks lib)
I Industry - 7 years in Machine Learning/AI startups
(mlboost: Python Machine Learning Boost lib)
I Founder QMining
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
AI and Machine Learning - Data-mining
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Machine Learning and Data-Mining
I Machine Learning: learn from data
I Data-mining: extracting patterns from data
I Machine Learning use extracted patterns to do prediction
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Machine Learning
I Learning from data
I Classification vs Clustering
I Applications: Attrition, Rank Customer (approve loans andcredit card),Fraud detection, Target-Marketing, Risk Analysis(insurance) etc.
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Supervised Learning (need class tag for each example)
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Unsupervised Learning - dimension reduction/clustering
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Learning Process
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Tanks in the desert
I Using ML requires insights
I An algo is only goods as its data
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Important Concepts
I Datasets (features + class)I Generalization vs OverfittingI Classification vs ClusteringI Features Quality (invariant and informative)
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
Bell Canada
I Find most probable interested clientsI 10000 most likely to buyI google mail
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
Microcell labs (Fido-Rogers)
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
PivotalPayments
I Find most probable clients to quit
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
Insurance
I Score customer risk of making a claim
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
Ubisoft-QMonitor
I Find incidents on serversI Find patterns (network, server, etc.)
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Target MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
QMiner - Global User Experience Incident Mining
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
What you should remember?
I No BI without Machine Learning
I Machine learning algorithms applications ⇑I goal = generalization⇒good prediction (DON’T OVERFIT)I 80-90% pre or post-processing + data visualization
I Python provide amazing integration
I **QMining is looking for intership students
I BI for Business User http://www.qlikview.com/
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?
Machine Learning 101Let’s dive into practical example
ConclusionQuestions?
Any questions?...intership 2011 ⇒ [email protected]://fraka6.blogspot.com/..Thanks,Francis [email protected]
Francis Pieraut [email protected] http://fraka6.blogspot.com/No BI without Machine Learning
OutlineWhy a talk about machine learning and BI?Machine Learning 101Supervised Learning (classification)Unsupervised Learning (clustering)Training and TestingImportant Concepts
Let's dive into practical exampleTarget MarketingCustomer behaviorRetentionRisk AnalysisMonitoring Root Cause Analysis - QMonitoringMonitoring Root Cause Analysis- QMiner
ConclusionQuestions?