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Microsoft Machine Learning Smackdown
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Microsoft Machine Learning Smackdown


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comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and …

comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and PredixionSoftware

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    • 1. Machine Learning Smackdown Lynn Langit May 7-9, 2014 | San Jose, CA
    • 2. Agenda Goal: Survey ML tools/methods that you can actually use on the Microsoft stack • Definitions • Tools I – Understanding 3rd party Excel Machine Learning Add-ins • Tools II – Using the Microsoft SQL Server SSAS & Data Mining Add-ins • Tools III – Using Predixion Software • Recap and Call To Action 3
    • 3. Terms Goal: Create common definitions of key terms • Business Analytics • Query • Aggregation • Predictive Analytics • Machine Learning • Statistics • Unsupervised Data Mining • Supervised Data Mining • Other 4
    • 4. What does the market look like now? 5 Regular Analytics Unsupervised DM Supervised DM Machine Learning
    • 5. CRISP DM Lifecycle applied to ML 6
    • 6. Machine Learning – an Example 7
    • 7. About 3rd party Excel Machine Learning Add-ins What are they? Toolbars in Excel – many different offerings • XLMiner • StatsMiner • XLStat • RExcel 8 Important: All of these tools assume expert statistical knowledge
    • 8. An aside…about R Language 9
    • 9. Viewing 3rd Party Add-ins XLMiner
    • 10. About the Data Mining Add-ins For Excel What is it? Free add-ins which add menus to use SSAS Analysis Services Data Mining • Table Analysis Tools for Excel • Use mining models with Excel data or external data • Data Mining Client for Excel • Create/test/explore/manage Mining Models • Data Mining Templates for Visio • Render/share mining models as Visio Drawings 11 Important: Use requires connection to SQL Server 2012 SSAS
    • 11. Using the Data Mining Add-ins for Excel DEMO
    • 12. Checking Understanding… Data Mining Structures • Containers for cleansed source data Data Mining Models • Child containers for source data plus one mining algorithm • SSAS Algorithms - Clustering, Time Series Prediction, Market-Basket Analysis, Text Mining and Neural Networks Model Verification, Processing and Usage Tools • Model query, Model processing 13
    • 13. About Predixion Software What is it? Suite of tools for predictive analytics • Insight Now • Use mining models with Excel data or external data • Insight Analytics • Create/test/explore/manage Mining Models • Insight Workbench • Prepare data for model creation • Web-based Viewers and Tools 14 Important: Runs as EITHER connected to SSAS on premise OR Connected to Predixion’s cloud-based servers
    • 14. Using Predixion Software DEMO
    • 15. 16
    • 16. Understanding options… 17 Add-in Server Required Complexity of install Other Cost of Add-in Cost of Solution XLMiner none easy Assumes stats expertise $$ $$ RExcel none easy Assumes R expertise $ $ Data Mining Add-ins SQL Server SSAS medium Designed for single user 0 $$$ Predixion on premise SQL Express easy Requires local R install 0 $$-$$$ Predixion on premise SQL Server SSAS medium Your data is stored locally 0 $$$$ Predixion cloud none easy Supports SSAS Data Mining AND R Language 0 $$-$$$
    • 17. 18 Machine Learning Skills Data Scientist Store Clean Aggregate ML Engineers Selects Libraries Applies Algorithms Creates Solutions ML Researcher Creates Algorithms
    • 18. Learning Paths – ML Developers Learn a language… DMX, PAX, R, Mahout, Julia Pick your IDE, tools… SSAS, Predixion, R-Studio, Wekka Pick a problem space… Marketing, Health, Financial Find (purchase)/gather/prepare some data… GO!  (Visualize results) 19
    • 19. Call to Action – ML Decision Makers • Pick one or more solutions • Gather source data • Prepare source data • Try out some data mining algorithms Evaluate it Understand it • Understand tooling costs • Understand learning costs • Understand data gathering costs • Understand data preparation costs • Understand data cleansing costs • Understand value of results 20
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    • 21. Thank You SoCalDevGal on