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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 [email protected] http://fraka6.blogspot.com/ 10 March 2011 MTI-820 ETS Francis Pieraut [email protected] http://fraka6.blogspot.com/ No BI without Machine Learning

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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?


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