Machine learning methods applied to big data – Business Intelligence Info

Therehasbeenanupsurgeinmachinelearningmethodsinrecentyears。

Growingevidencesuggeststhatmachinelearningiswhatalotofpeopledowiththebigdatatheyhaveaccumulated。

Likeanycomplexundertaking,itisworthwhiletobreakitdownintocomponentparts。

ThatistheobjectiveofthisepisodeoftheTalkingDatapodcast,inwhichTechTargetreportersJackVaughanandEdBurnsdiscusstheevolutionofmachinelearningthroughthelensoftechnologiesemployedandend-useapplications。

Amongusecasescitedareriskestimationininsurance,creditscoringanddigitaladplacement。

Thewidespaninapplicationscan,inturn,leadtoabroadvarietyofsystems,asdifferentamountsandtypesofdatafeedavarietyofmachinelearningalgorithmsthatiterativelypredictlikelyoutcomesandtesttheiroutputagainstknowngoodresultsthathavealreadybeenrecorded。

Thepodcastbrieflycoversmachinelearningrootsinstatistics。

Infact,suchbread-and-buttermethodsasNa?

veBayesianfiltersgobackatleastasfarasthe1950s,whentranslatingsuchfunctionstocomputerlanguagewasalabor-intensivetask。

Thesedays,thesoftwareanddatainfrastructureavailableforapplyingthatfilterhavechangedalot。

Inthe1990s,machinelearningmethodsbegantoappearinthedisciplineknownasdataminingthatbecamepartofapplicationsrunningonmajorrelationaldatabasesanddatawarehousesystems。

Presently,toolslikeH20,SparkandMahoutcomeintoplay,runningondistributeddatainfrastructure。

AlsodiscussedisAmazonMachineLearningsoftware,whichenablesitsuserstolaunchtestapplicationsinthecloudwithoutconfiguringhardwareorsoftwareinfrastructure。

CheckoutthisepisodeoftheTalkingDatapodcastandlearnmoreaboutmachinelearningmethodsthatstandpoisedtochangethecourseofdataanalytics。

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