Machine Learning Engineer

SUPRIYATELU

MACHINELEARNINGENGINEER

WORKHISTORY:

MachineLearningEngineer/2018-current

Identifiednewproblemareasandresearchedtechnicaldetailstobuildinnovativeproducts andsolutions.

Createdcustomizedapplicationstomakecriticalpredictions,automatereasoningand decisions,andcalculateoptimizationalgorithms.

Transformedrawdatatoconformtoassumptionsofmachinelearningalgorithm.

AnalysisofdataandRunningmachineLearningexperimentsusingaprogramminglanguage withmachinelearningalgorithms.

Deployingmachinelearningsolutionsintoproduction.

Datacollection,DataCleaning(convertintostructuredata),choosingtherightmachine learningmodel,DataVisualizationandDeployment.

Studiednewtechnologiestosupportmachinelearningapplicationsi.e,NaturalLanguage Processing.

PROFESSIONALSUMMARY:

I'mSophisticatedMachineLearningEngineerwithbackgroundinindependentresearchusing intuitive,web-basedarchitecture.SkilledinwithDocumentedhistoryofdiscoveringmethodsto intelligentlyusedatatoenhanceuserexperience.Effectivelyresearchestechniquesfordifferent approachtosolveaproblemfrokscratchdatacollectiontodeployingapplicationintoproduction yieldinginsightstoexpandscustomerconsciousness.

970-***-****

*******.******@*****.***

EDUCATION

BachelorofTechnology:ElectronicsandCommunicationEngineering/2014-2018achievedfirst classwithdistinctionfromGokarajuRangarajuInstituteofEngineeringandTechnology, Hyderabad,Telangana.

SKILLS

NaturalLanguage

processing

MachineLearning

Python

SQLforData

science

FlaskWebFrame

work.

Deployment

Documentation

Skills

MSOffice

Familiarwith

Linuxand

Windows.

PROJECTS:

1.RECOMMENDATIONSYSTEM:

PROBLEMSTATEMENT:BasedonInternalDocumentContent

RecommendingAuditObservationstotheuserwhichtheuser mayfaceinfuture.

Tools:IftheDocumentisPDF,extractingtextbythefollowing toolslikeImagemagick,Ghostscript,OCR(Tesseract-ocr) Librariesused:Pandas,numpy,sklearn,textract,wand,

tesseract,ghostscript,os,nltk,TF-IDFTransformer,count vectorizer,

APPROACH:MainAimistoExtractTextfromDocument(PDF/

docx/doc).AfterTextextractionusingNaturalLanguage

processingbasedonkeywordspresentinthecontentand

observationsrecommendingobservationstothecurrent

document.

2.AuditSimilarObservations:Fetchingthesimilar

observationsbasedonAuditorgivenobservationwithprevious historicaldata.

3.RecommendingTrainingtotheEmployeebasedonJobRole

andJobDescriptionbasedonpreviousdata.

4.RecommenderSystem:RecommendingDocumenttotrainee

basedonthecurrentdocumentlikeyoutuberecommendation whenwewatchvideo,wegetrecommendationbasedongenre

wewatch.

4.AuditSuccessrateprediction:BasedontheAuditordata, predictingwhetherauditissuccessornotbasedon

consideringfeaturesliketypeofobservation(critical,major, minoretc.),repeatedobservations,departmentandsite. 5.FeedbackAnalytics:basedonthetypeoffeedbackquestion, trainerorcourse,ratingofcourseandalsotrainerbasedonthe availabledata.

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