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Machine learning course

Attend the Best Machine learning training Courses in Bangalore From ExcelR. Practical Machine learningTraining Sessions with Assured Placement From Excelr Solutions.<br><br><a href=u201d https://www.excelr.com/machine-learning-course-training-in-bangaloreu201d> machine learning course</a><br>

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Machine learning course

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  1. MachineLearning

  2. NeuralNetworks

  3. Understandingneuralnetworks AnArtificialNeuralNetwork(ANN)modelstherelationshipbetweenasetofinputsignalsandanoutputsignalusingamodelderivedfromourunderstandingofhowabiologicalbrainrespondstostimulifromsensoryinputs.Justasabrainusesanetworkofinterconnectedcellscalledneuronstocreateamassiveparallelprocessor,ANNusesanetworkofartificialneuronsornodestosolvelearningproblems Thehumanbrainismadeupofabout85billionneurons,resultinginanetworkcapableofrepresentingatremendousamountofknowledge Forinstance,acathasroughlyabillionneurons,amousehasabout75millionneurons,andacockroachhasonlyaboutamillionneurons.Incontrast,manyANNscontainfarfewerneurons,typicallyonlyseveralhundred,sowe'reinnodangerofcreatinganartificialbrainanytimeinthenearfuture

  4. Biologicaltoartificialneurons Incomingsignalsarereceivedbythecell'sdendritesthroughabiochemicalprocess.Theprocessallowstheimpulsetobeweightedaccordingtoitsrelativeimportanceorfrequency.Asthecellbodybeginsaccumulatingtheincomingsignals,athresholdisreachedatwhichthecellfiresandtheoutputsignalistransmittedviaanelectrochemicalprocessdowntheaxon.Attheaxon'sterminals,theelectricsignalisagainprocessedasachemicalsignaltobepassedtotheneighbouringneurons.

  5. Thisdirectednetworkdiagramdefinesarelationshipbetweentheinputsignalsreceivedbythedendrites(xvariables),andtheoutputsignal(yvariable).Justaswiththebiologicalneuron,eachdendrite'ssignalisweighted(wvalues)accordingtoitsimportance.TheinputsignalsaresummedbythecellbodyandthesignalispassedonaccordingtoanactivationfunctiondenotedbyfThisdirectednetworkdiagramdefinesarelationshipbetweentheinputsignalsreceivedbythedendrites(xvariables),andtheoutputsignal(yvariable).Justaswiththebiologicalneuron,eachdendrite'ssignalisweighted(wvalues)accordingtoitsimportance.Theinputsignalsaresummedbythecellbodyandthesignalispassedonaccordingtoanactivationfunctiondenotedbyf Atypicalartificialneuronwithninputdendritescanberepresentedbytheformulathatfollows.Thewweightsalloweachoftheninputs(denotedbyxi)tocontributeagreaterorlesseramounttothesumofinputsignals.Thenettotalisusedbytheactivationfunctionf(x),andtheresultingsignal,y(x),istheoutputaxon

  6. Inbiologicalsense,theactivationfunctioncouldbeimaginedasaprocessthatinvolvessummingthetotalinputsignalanddeterminingwhetheritmeetsthefiringthreshold.Ifso,theneuronpassesonthesignal;otherwise,itdoesnothing.InANNterms,thisisknownasathresholdactivationfunction,asitresultsinanoutputsignalonlyonceaspecifiedinputthresholdhasbeenattainedInbiologicalsense,theactivationfunctioncouldbeimaginedasaprocessthatinvolvessummingthetotalinputsignalanddeterminingwhetheritmeetsthefiringthreshold.Ifso,theneuronpassesonthesignal;otherwise,itdoesnothing.InANNterms,thisisknownasathresholdactivationfunction,asitresultsinanoutputsignalonlyonceaspecifiedinputthresholdhasbeenattained Thefollowingfiguredepictsatypicalthresholdfunction;inthiscase,theneuronfireswhenthesumofinputsignalsisatleastzero.Becauseitsshaperesemblesastair,itissometimescalledaunitstepactivationfunction

  7. Networktopology • Theabilityofaneuralnetworktolearnisrootedinitstopology,or • thepatternsandstructuresofinterconnectedneurons • keycharacteristics • Thenumberoflayers • Whetherinformationin thenetworkisallowedto travel backward • Thenumberofnodeswithineachlayer of thenetwork

  8. Numberoflayers Theinputandoutputnodesarearrangedingroupsknownaslayers Inputnodesprocesstheincomingdataexactlyasitisreceived,thenetworkhasonlyonesetofconnectionweights(labeledhereasw1,w2,andw3).Itisthereforetermedasingle-layernetwork

  9. SupportVectorMachines

  10. ASupportVectorMachine(SVM)canbeimaginedasasurfacethatcreatesaboundarybetweenpointsofdataplottedinmultidimensionalthatrepresentexamplesandtheirfeaturevaluesASupportVectorMachine(SVM)canbeimaginedasasurfacethatcreatesaboundarybetweenpointsofdataplottedinmultidimensionalthatrepresentexamplesandtheirfeaturevalues ThegoalofaSVMistocreateaflatboundarycalledahyperplane,which dividesthespacetocreatefairlyhomogeneouspartitionsoneitherside SVMscanbeadaptedforusewithnearlyanytypeoflearningtask, includingbothclassificationandnumericprediction

  11. Classificationwithhyperplanes Forexample,thefollowingfiguredepictshyperplanesthatseparategroupsofcirclesandsquaresintwoandthreedimensions.Becausethecirclesandsquarescanbeseparatedperfectlybythestraightlineorflatsurface,theyaresaidtobelinearlyseparable

  12. Which isthe“best” Fit! Intwodimensions,thetaskoftheSVMalgorithmistoidentifyalinethatseparatesthetwoclasses.Asshowninthefollowingfigure,thereismorethanonechoiceofdividinglinebetweenthegroupsofcirclesandsquares.Howdoesthealgorithmchoose

  13. Usingkernelsfornon-linearspaces AkeyfeatureofSVMsistheirabilitytomaptheproblemintoahigherdimensionspaceusingaprocessknownasthekerneltrick.Indoingso,anonlinearrelationshipmaysuddenlyappeartobequitelinear. Afterthekerneltrickhasbeenapplied,welookatthedatathroughthelensofanewdimension:altitude.Withtheadditionofthisfeature,theclassesarenowperfectlylinearlyseparable

  14. Thankyou

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