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Microsoft Machine Learning Smackdown

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Information about Microsoft Machine Learning Smackdown
Technology

Published on March 13, 2014

Author: lynnlangit

Source: slideshare.net

Description

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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Machine Learning Smackdown Lynn Langit May 7-9, 2014 | San Jose, CA

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

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

What does the market look like now? 5 Regular Analytics Unsupervised DM Supervised DM Machine Learning

CRISP DM Lifecycle applied to ML 6

Machine Learning – an Example 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

An aside…about R Language 9

Viewing 3rd Party Add-ins XLMiner

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

Using the Data Mining Add-ins for Excel DEMO

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

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

Using Predixion Software DEMO

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 $$$ Predixion on premise SQL Server SSAS hard Your data is stored locally 0 $$$$ Predixion cloud none easy Supports R Language 0 $$$$

18 Machine Learning Skills Data Scientist Store Clean Aggregate ML Engineers Selects Libraries Applies Algorithms Creates Solutions ML Researcher Creates Algorithms

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

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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Thank You SoCalDevGal on

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