CLASSIFICATION, MODELS AND APPLICATIONS OF MACHINE LEARNING

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DOI:10.21884/IJMTER.2020.7001.WMKJN 1

CLASSIFICATION, MODELS AND APPLICATIONS

OF MACHINE LEARNING

Haripriya Konduru1, Dr. Gaurav Sharma2

1

B.Tech. Student, ECE Dept., CVR College of Engineering, Hyderabad, Telangana, India

2

Asst. Prof., ECE Dept., CVR College of Engineering, Hyderabad, Telangana, India

Abstract:-Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: “The ability to learn”. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning. Machine learning is actively being used today, perhaps in many more places than one would expect. The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change.

Keywords :- Machine Learning, Artificial Intelligence, Artificial Neural Network, Classification

I. INTRODUCTION

The iterm iMachine iLearning iwas icoinediby iArthur iSamuel iin i1959, ian iAmerican ipioneer iin ithe ifield iof icomputer igaming iand iartificial iintelligence iand istated ithat i“it igives icomputers ithe iability ito ilearn iwithout ibeing iexplicitly iprogrammed”.iAnd iin i1997, iTom iMitchell igave ia i“well-posed” imathematical iand irelational idefinition ithat i“A icomputer iprogram iis isaid ito ilearn ifrom iexperience iE iwith irespect ito isome itask iT iand isome iperformance imeasure iP, iif iits iperformance ion iT, ias imeasured iby iP, iimproves iwith iexperience iE.iMachine iLearning iis ia ilatest ibuzzword ifloating iaround.iIt ideserves ito, ias iit iis ione iof ithe imost iinteresting isub-field iof iComputer iScience.[1]

So, iwhat idoes iMachine iLearning ireally imean?

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International Journal of Modern Trends in Engineering and Research (IJMTER)

Volume 07, Issue 1, [January– 2020] ISSN (Online):2349–9745; ISSN (Print):2393-8161

Machine ilearning iis iclosely irelated ito icomputational istatistics, iwhich ifocuses ion imaking ipredictions iusing icomputers.iThe istudy iof imathematical ioptimization idelivers imethods, itheory iand iapplication idomains ito ithe ifield iof imachine ilearning.iData imining iis ia ifield iof istudy iwithin imachine ilearning, iand ifocuses ion iexploratory idata ianalysis ithrough iunsupervised ilearning[2].iIn iits iapplication iacross ibusiness iproblems, imachine ilearning iis ialso ireferred ito ias ipredictive ianalytics.

The ihighly icomplex inature iof imany ireal-world iproblems, ithough, ioften imeans ithat iinventing ispecialized ialgorithms ithat iwill isolve ithem iperfectly ievery itime iis iimpractical, iif inot iimpossible.iExamples iof imachine ilearning iproblems iinclude, i“Is ithisicancer?”, i“Which iof ithese ipeople iare igood ifriends iwith ieach iother?”, i“Will ithis iperson ilike ithis imovie?” isuch iproblems iare iexcellent itargets ifor iMachine iLearning, iand iin ifact imachine ilearning ihas ibeen iapplied isuch iproblems iwith igreat isuccess i[3].

II. CLASSIFICATION iOF iMACHINE iLEARNING

Machine ilearning iimplementations iare iclassified iinto ithree imajor icategories, idepending ion ithe inature iof ithe ilearning i“signal” ior i“response” iavailable ito ia ilearning isystem iwhich iare ias ifollows, ias ishown iiniFig(1).

 Supervised ilearning i

 Unsupervised ilearning

 Semi-supervised ilearning

 Reinforcement ilearning

Fig.1 iClassification iof imachine ilearning

III. CATEGORIZING iON iTHE iBASIS iOF iREQUIRED iOUTPUT

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International Journal of Modern Trends in Engineering and Research (IJMTER)

Fig.2 i iCategories iof imachine ilearning

 Classification: iWhen iinputs iare idivided iinto itwo ior imore iclasses, iand ithe ilearner imust iproduce ia imodel ithat iassigns iunseen iinputs ito ione ior imorei(multi-label iclassification) iof ithese iclasses i[3].iThis iis itypically itackled iin ia isupervised iway.iSpam ifiltering iis ian iexample iof iclassification, iwhere ithe iinputs iare iemail i(or iother) imessages iand ithe iclasses iare i“spam” iand i“not ispam”.

 Regression: iWhich iis ialsoia isupervised iproblem, iA icase iwhen ithe ioutputs iare icontinuous irather ithan idiscrete.

 Clustering: iWhenia iset iof iinputs iis ito ibe idivided iinto igroups.iUnlike iin iclassification, ithe igroups iare inot iknown ibeforehand, imaking ithis itypically ian iunsupervised itask i[4, i5].

IV. EXPLANATION iTO iUNDERSTAND iCLASSIFICATIONS

 Supervised iLearning

Whenian ialgorithm ilearns ifrom iexample idata iand iassociated itarget iresponses ithat ican iconsist iof inumeric ivalues ior istring ilabels, isuch ias iclasses ior itags, iin iorder ito ilater ipredict ithe icorrect iresponse iwhen iposed iwith inew iexamples icomes iunder ithe icategory iof iSupervised ilearning.iThis iapproach iis iindeed isimilar ito ihuman ilearning iunder ithe isupervision iof ia iteacher[3].iThe iteacher iprovides igood iexamples ifor ithe istudent ito imemorize, iand ithe istudent ithen iderives igeneral irules ifrom ithese ispecific iexamples.iIn ithis imethod, ithe itraining idata iwhich iis ifed iin ito ithe isystem ialso iincludes ia ilabel iwhich istates ithe idesired isolution ifor ieach iinstance.iAs ishown iin iFig(3).

Fig.3 iSupervised iLearning

The data set is mainly divided into two sections such as Training and testing data (Evaluation data). The training set is used to fine tune the Model or Hypothesis. This hypothesis is none other than a mathematical representation of the training method. During the model evaluation, it analyses the error (the difference between the expected value and the value been output by the model) and the learning’s objective is to fine-tune the model in-order to get the minimum error. The training been evaluated using the training set. After that we can use our real data to perform the desired functionality (prediction, classification etc). There are numerous algorithms involved in this and I am hoping to give you a comprehensive coverage about these in upcoming blogs.

A itypical isupervised ilearning itask iis iclassificationi[6].iThe ispam ifilter iis ia igood iexample iof ithis: iit iis itrained iwith imany iexamples iare iemails ialong iwith itheir iclass i(spam ior iham), iand iit imust ilearn ihow ito iclassify inew iemails.i

 Unsupervised iLearning

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irestructure ithe idata iinto isomething ielse, isuch ias inew ifeatures ithat imay irepresent ia iclass ior ia inew iseries iof iun-correlated ivalues.iThey iare iquite iuseful iin iproviding ihumans iwith iinsights iinto ithe imeaning iof idata iand inew iuseful iinputs ito isupervised imachine ilearning ialgorithms i[5].iAs ia ikind iof ilearning, iit iresembles ithe imethods ihumans iuse ito ifigure iout ithat icertain iobjects ior ievents iare ifrom ithe isame iclass, isuch ias iby iobserving ithe idegree iof isimilarity ibetween iobjects.iSome irecommendation isystems ithat iyou ifind ion ithe iweb iin ithe iform iof imarketing iautomation iare ibased ion ithis itype iof ilearning i[7].

This iis ithe itotal iopposite iof iSupervised iLearning.iHere ithe itraining idata idoes inot iinclude ilabels.iThe isystem ihas ito ilearn ion iits iown.iIt iis imainly icontains iwith itechniques ithat iinvolves ithe igrouping iof idata ipoints.iFor ian iexample, isay iyou ihave ia ilot iof idata iabout iyour icustomers ito iyour iweb isite ior isupermarket.iYou imay iwant ito irun ia iclustering ialgorithm ito itry ito idetect igroups iof isimilar icustomers.iUsing ithis itechnique iyou ican iidentify iwhat ikind iof icustomers ithat iyou iinvolved iwith imostly iand iyou imay iwould ithrow iout ia imarketing icampaign ibased ion ithat iclassification.iAs ishown iin iFig(4).

Yet ianother iimportant iunsupervised itask iis ianomaly idetectioni[8, i9].iExamples ifor ithese iare ilike idetecting icredit icard ifraud, iidentifying imanufacturing idefects, idetecting iand icleaning idata iin ibig idata ianalysis ietc.

Fig.4 i iUnsupervised iLearning

 Semi-supervised iLearning

There iisia ispecial icase iof ithis iprinciple iknown ias iTransduction iwhere ithe ientire iset iof iproblem iinstances iis iknown iat ilearning itime, iexcept ithat ipart iof ithe itargets iare imissing.iThis iis iwhen imost iof ithe itraining idata iare iunlabelled iwhile ithe ibalance ifew iare ilabelled.iSo imost iof ithe iSemi iSupervised iLearning ialgorithms iconsist iof ia icombination iof iSupervised iand iUnsupervised ialgorithms.iGoogle iphotos iis ione iof ithe ibest iexamples ifor iSemi iSupervised iLearning i[10].iWhen iwe ilabel ione iperson’s iname iin ia iphotograph, iGoogle iphotos ican ishow iall ithe iphotos ithat iperson iis iin, ibecause iof iits iface irecognition iability.iAs ishown iin iFig(5)

Fig.5 i i iSemi-supervised iLearning ivs.iSupervised iLearning

 Reinforcement iLearning

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ithe isolution ithe ialgorithm iproposes icomes iunder ithe icategory iof iReinforcement ilearning, iwhich iis iconnected ito iapplications ifor iwhich ithe ialgorithm imust imake idecisions i(so ithe iproduct iis iprescriptive, inot ijust idescriptive, ias iin iunsupervised ilearning), iand ithe idecisions ibear iconsequences.iIn ithe ihuman iworld, iit iis ijust ilike ilearning iby itrial iand ierror.iErrors ihelp iyou ilearn ibecause ithey ihave ia ipenalty iadded i(cost, iloss iof itime, iregret, ipain, iand iso ion), iteaching iyou ithat ia icertain icourse iof iaction iis iless ilikely ito isucceed ithan iothers i[11].iAn iinteresting iexample iof ireinforcement ilearning ioccurs iwhen icomputers ilearn ito iplay ivideo igames iby ithemselves.iIn ithis icase, ian iapplication ipresents ithe ialgorithm iwith iexamples iof ispecific isituations, isuch ias ihaving ithe igamer istuck iin ia imaze iwhile iavoiding ian ienemy.iThe iapplication ilets ithe ialgorithm iknow ithe ioutcome iof iactions iit itakes, iand ilearning ioccurs iwhile itrying ito iavoid iwhat iit idiscovers ito ibe idangerous iand ito ipursue isurvival i[6].iWe ican ihave ia ilook iat ihow ithe icompany iGoogle iDeepMind ihas icreated ia ireinforcementilearning iprogram ithat iplays iold iAtari’s ivideogames.iWhen iwatching ithe ivideo, inotice ihow ithe iprogram iis iinitially iclumsy iand iunskilled ibut isteadily iimproves iwith itraining iuntil iit ibecomes ia ichampion.iSee iFig(6).

i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i

Fig.6 iReinforcement iLearning

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International Journal of Modern Trends in Engineering and Research (IJMTER)

Fig.7 iAlphaGo i iVs.iLee iSedol

 Self-Learning

Self-learning ias imachine ilearning iparadigm iwas iintroduced iin i1982 ialong iwith ia ineural inetwork icapable iof iself-learning inamed iCrossbar iAdaptive iArrayi(CAA).iIt iis ia ilearning iwith ino iexternal irewards iand ino iexternal iteacher iadvices.iThe iCAA iself-learning ialgorithm icomputes, iin ia icrossbar ifashion, iboth idecisions iabout iactions iand iemotions i(feelings) iabout iconsequence isituations.iThe isystem iis idriven iby ithe iinteraction ibetween icognition iand iemotion.iThe iself-learning ialgorithm iupdates ia imemory imatrix iW i=||w i(a, is) i|| isuch ithat iin ieach iiteration iexecutes ithe ifollowing imachine ilearning iroutine:

In isituation is iperform iactionia; iReceive iconsequence isituation is’;

iCompute iemotion iof ibeing iin iconsequence isituation iv(s’); iUpdate icrossbar imemory iw’ i(a, is) i= iw i(a, is) i+ iv(s’).

It is a system with only one input, situation s, and only one output, action (or behaviour) a. There is neither a separate reinforcement input nor an advice input from the environment[7]. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioural environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioural environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behaviour, in an environment that contains both desirable and undesirable situations. As shown in Fig(8).

Fig. i8 iSelf-Learning

 Feature ilearning

Feature iengineering iis ithe iprocess iof iusing idomain iknowledge iof ithe idata ito icreate ifeatures ithat imake imachine ilearning ialgorithms iwork.iFeature iengineering iis ifundamental ito ithe iapplication iof imachine ilearning, iand iis iboth idifficult iand iexpensive[9].iThe ineed ifor imanual ifeature iengineering ican ibe iobviated iby iautomated ifeature ilearning.iFeature iengineering iis ian iinformal itopic, ibut iit iis iconsidered iessential iin iapplied imachine ilearning.

 Sparse idictionary ilearning

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iexample iis iassociated iwith ithe iclass ithat iis ibest isparsely irepresented iby ithe icorresponding idictionary.iSparse idictionary ilearning ihas ialso ibeen iapplied iin iimage ide-noising.iThe ikey iidea iis ithat ia iclean iimage ipatch ican ibe isparsely irepresented iby ian iimage idictionary, ibut ithe inoise icannot i[12].

 Anomaly idetection ilearning

In idata imining, ianomaly idetection, ialso iknownias ioutlier idetection, iis ithe iidentification iof irare iitems, ievents ior iobservations iwhich iraise isuspicions iby idiffering isignificantly ifrom ithe imajority iof ithe idata.iTypically, ithe ianomalous iitems irepresent ian iissue isuch ias ibank ifraud, ia istructural idefect, imedical iproblems ior ierrors iin ia itext.iAnomalies iare ireferred ito ias ioutliers, inovelties, inoise, ideviations iand iexceptions.iIn iparticular, iin ithe icontext iof iabuse iand inetwork iintrusion idetection, ithe iinteresting iobjects iare ioften inot irare iobjects, ibut iunexpected ibursts iin iactivity.iThis ipattern idoes inot iadhere ito ithe icommon istatistical idefinition iof ian ioutlier ias ia irare iobject, iand imany ioutlier idetection imethods i(in iparticular, iunsupervised ialgorithms) iwill ifail ion isuch idata, iunless iit ihas ibeen iaggregated iappropriately.iInstead, ia icluster ianalysis ialgorithm imay ibe iable ito idetect ithe imicro-clusters iformed iby ithese ipatterns.iAs ishown iin iFig(9).

iThree ibroad icategories iof ianomaly idetection itechniques iexist.iUnsupervised ianomaly idetection itechniques idetect ianomalies iin ian iunlabelled itest idata iset iunder ithe iassumption ithat ithe imajority iof ithe iinstances iin ithe idata iset iare inormal, iby ilooking ifor iinstancesithat iseem ito ifit ileast ito ithe iremainder iof ithe idata iset.iSupervised ianomaly idetection itechniques irequire ia idata iset ithat ihas ibeen ilabelled ias i"normal" iand i"abnormal" iand iinvolves itraining ia iclassifier i(the ikey idifference ito imany iother istatistical iclassification iproblems iis ithe iinherently iunbalanced inature iof ioutlier idetection).iSemi-supervised ianomaly idetection itechniques iconstruct ia imodel irepresenting inormal ibehaviour ifrom ia igiven inormal itraining idata iset iand ithen itest ithe ilikelihood iof ia itest iinstance ito ibe igenerated iby ithe imodel i[13].

Fig. i9 i i iAnomaly idetection

 Association irule ilearning

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i[14].iThey iseek ito iidentify ia iset iof icontext-dependent irules ithat icollectively istore iand iapply iknowledge iin ia ipiecewise imanner iin iorder ito imake ipredictions.iInductive ilogic iprogramming i(ILP) iis ian iapproach ito irule-learning iusing ilogic iprogramming ias ia iuniform irepresentation ifor iinput iexamples, ibackground iknowledge, iand ihypotheses.iSee iFig(10).

Fig.10 i i i i i iAssociation iRule iLearning

V. MODELS iIN iMACHINE iLEARNING

Performing imachine ilearning iinvolves icreatingia imodel, iwhich iis itrained ion isome itraining idata iand ithen ican iprocess iadditional idata ito imake ipredictions.iVarious itypes iof imodels ihave ibeen iused iand iresearched ifor imachine ilearning isystems

 Artificial ineural inetworks

Artificial ineural inetworksi(ANNs), ior iconnectionist isystems, iare icomputing isystems ivaguely iinspired iby ithe ibiological ineural inetworks ithat iconstitute ianimal ibrains.iSuch isystems i"learn" ito iperform itasks iby iconsidering iexamples, igenerally iwithout ibeing iprogrammed iwith iany itask-specific irules.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another [15]. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold.

i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i

Fig.11 iArtificial iNeural iNetworks

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ilayer i(the ioutput ilayer), ipossibly iafter itraversing ithe ilayers imultiple itimes.iThe ioriginal igoal iof ithe iANN iapproach iwas ito isolve iproblems iin ithe isame iway ithat ia ihuman ibrain iwould.iHowever, iover itime, iattention imoved ito iperforming ispecific itasks, ileading ito ideviations ifrom ibiology.iArtificial ineural inetworks ihave ibeen iused ion ia ivariety iof itasks, iincluding icomputer ivision, ispeech irecognition, imachine itranslation, isocial inetwork ifiltering, iplaying iboard iand ivideo igames iand imedical idiagnosis.iAs ishown iin iFig(11).i

 Decision itrees

Decision itree ilearning iuses ia idecision itree ias ia ipredictive imodel ito igo ifrom iobservations iabout ian iitemi(represented iin ithe ibranches) ito iconclusions iabout ithe iitem's itarget ivalue i(represented iin ithe ileaves).iIt iis ione iof ithe ipredictive imodelling iapproaches iused iin istatistics, idata imining iand imachine ilearning.iTree imodels iwhere ithe itarget ivariable ican itake ia idiscrete iset iof ivalues iare icalled iclassification itrees; iin ithese itree istructures, ileaves irepresent iclass ilabels iand ibranches irepresent iconjunctions iof ifeatures ithat ilead ito ithose iclass ilabels.iDecision itrees iwhere ithe itarget ivariable ican itake icontinuous ivalues i(typically ireal inumbers) iare icalled iregression itrees.iIn idecision ianalysis, ia idecision itree ican ibe iused ito ivisually iand iexplicitly irepresent idecisions iand idecision imaking.iIn idata imining, ia idecision itree idescribes idata, ibut ithe iresulting iclassification itree ican ibe ian iinput ifor idecision imaking.iAs ishown iin iFig(12).

Fig. i12 iExample ifor iDecision itree

 Support ivector imachines

Support ivector imachinesi(SVMs), ialso iknown ias isupport ivector inetworks, iare ia iset iof irelated isupervised ilearning imethods iused ifor iclassification iand iregression.iGiven ia iset iof itraining iexamples, ieach imarked ias ibelonging ito ione iof itwo icategories, ian iSVM itraining ialgorithm ibuilds ia imodel ithat ipredicts iwhether ia inew iexample ifalls iinto ione icategory ior ithe iother.iAs ishown iin iFig(13).iAn iSVM itraining ialgorithm iis ia inon-probabilistic, ibinary, ilinear iclassifier, ialthough imethods isuch ias iPlatt iscaling iexist ito iuse iSVM iin ia iprobabilistic iclassification isetting.iIn iaddition ito iperforming ilinear iclassification, iSVMs ican iefficiently iperform ia inon-linear iclassification iusing iwhat iis icalled ithe ikernel itrick, iimplicitly imapping itheir iinputs iinto ihigh-dimensional ifeature ispaces i[16].

Fig. i13 iSupport iVector iMachines

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Regression ianalysis iencompassesia ilarge ivariety iof istatistical imethods ito iestimate ithe irelationship ibetween iinput ivariables iand itheir iassociated ifeatures.iIts imost icommon iform iis ilinear iregression, iwhere ia isingle iline iis idrawn ito ibest ifit ithe igiven idata iaccording ito ia imathematical icriterion isuch ias iordinary ileast isquares.iThe ilatter iis ioftentimes iextended iby iregularization i(mathematics) imethods ito imitigate ioverfitting iand ihigh ibias, ias ican ibe iseen iin iridge iregression.iWhen idealing iwith inon-linear iproblems, igo-to imodels iinclude ipolynomial iregression, iLogistic iregression i(often iused iin istatistical iclassification) ior ieven ikernel iregression, iwhich iintroduces inon-linearity iby itaking iadvantage iof ithe ikernel itrick ito iimplicitly imap iinput ivariables ito ihigher idimensional ispace.iAs ishown iin iFig(14).

Fig.14 iRegression iAnalysis

 Bayesian inetworks

A iBayesian inetwork, ibelief inetwork ior idirected iacyclic igraphical imodel iis ia iprobabilistic igraphical imodel ithat irepresents ia iset iof irandom ivariables iand itheir iconditional iindependence iwith ia idirected iacyclic igraphi(DAG).iFor iexample, ia iBayesian inetwork icould irepresent ithe iprobabilistic irelationships ibetween idiseases iand isymptoms i[17].iGiven isymptoms, ithe inetwork ican ibe iused ito icompute ithe iprobabilities iof ithe ipresence iof ivarious idiseases.iEfficient ialgorithms iexist ithat iperform iinference iand ilearning.iBayesian inetworks ithat imodel isequences iof ivariables, ilike ispeech isignals ior iprotein isequences, iare icalled idynamic iBayesian inetworks.iAs ishown iin iFig(16).i iGeneralizations iof iBayesian inetworks ithat ican irepresent iand isolve idecision iproblems iunder iuncertainty iare icalled iinfluence idiagrams.

Fig.16iA isimple iBayesian inetwork.iRain iinfluences iwhether ithe isprinkler iis iactivated, iand iboth irain iand ithe isprinkler iinfluence iwhether ithe igrass iis iwet.

 Genetic ialgorithm i

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Fig. i17 iGenetic ialgorithm

 Training imodels

Usually, imachine ilearning imodels irequire ia ilot iof idata iin iorder ifor ithem ito iperformiwell[11].iUsually, iwhen itraining ia imachine ilearning imodel, ione ineeds ito icollect ia ilarge, irepresentative isample iof idata ifrom ia itraining iset.iData ifrom ithe itraining iset ican ibe ias ivaried ias ia icorpus iof itext, ia icollection iof iimages, iand idata icollected ifrom iindividual iusers iof ia iservice.iOverfitting iis isomething ito iwatch iout ifor iwhen itraining ia imachine ilearning imodel.iAs ishown iin iFig(18).

 Federated ilearning

Federated ilearning iis ia inew iapproach ito itraining imachine ilearning imodels ithat idecentralizes ithe itraining iprocess, iallowing ifor iusers' iprivacy ito ibe imaintained iby inot ineeding ito isend itheir idata ito ia icentralized iserver.[12] iThis ialso iincreases iefficiency iby idecentralizing ithe itraining iprocess ito imany idevices.iFor iexample, iGboard iuses ifederated imachine ilearning ito itrain isearch iquery iprediction imodels ion iusers' imobile iphones iwithout ihaving ito isend iindividual isearches iback ito iGoogle.iAs ishown iin iFig(19).

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Fig. i19 iFederated iLearning

VI. APPLICATIONS

Machine ilearning iis iactively ibeing iused itoday, iperhaps iin imany imore iplaces ithan ione iwould iexpect. iWe i i iprobably i i iuse i ia i ilearning i ialgorithm idozens iof itime iwithout ieven iknowing iit.iApplications iof iMachine iLearning iinclude:[3]

 Web iSearch iEngine: iOne iof ithe ireasons iwhy isearch iengines ilike igoogle, ibing ietc iwork iso iwell iis ibecause ithe isystem ihas ilearnt ihow ito irank ipages ithroughia icomplex ilearning ialgorithm.

 Photo itagging iApplications: iBe iit ifacebook ior iany iother iphoto itagging iapplication, ithe iability ito itag ifriends imakes iit ieven imore ihappening.iIt iis iall ipossible ibecause iof ia iface irecognition ialgorithm ithat iruns ibehind ithe iapplication.

 Spam iDetector: iOur imail iagent ilike iGmail ior iHotmail idoesia ilot iof ihard iwork ifor ius iin iclassifying ithe imails iand imoving ithe ispam imails ito ispam ifolder.iThis iis iagain iachieved iby ia ispam iclassifier irunning iin ithe iback iend iof imail iapplication.

Today, icompanies iare iusing iMachine iLearning ito iimprove ibusiness idecisions, iincrease iproductivity, idetect idisease, iforecast iweather, iand ido imany imore ithings.iWith ithe iexponential igrowth iof itechnology, iwe inot ionly ineed ibetter itools ito iunderstand ithe idata iwe icurrently ihave, ibut iwe ialso ineed ito iprepare iourselves ifor ithe idata iwe iwill ihave.iTo iachieve ithis igoal iwe ineed ito ibuild iintelligent imachines.iWe ican iwrite ia iprogram ito ido isimple ithings.iBut ifor imost iof itimes iHardwiring iIntelligence iin iit iis idifficult.iBest iway ito ido iit iis ito ihave isome iway ifor imachines ito ilearn ithings ithemselves.iA imechanism ifor ilearning i– iif ia imachine ican ilearn ifrom iinput ithen iit idoes ithe ihard iwork ifor ius.iThis iis iwhere iMachine iLearning icomes iin iaction.iSome iexamples iof imachine ilearning iare:

 Database iMining ifor igrowth iof iautomation: iTypical iapplications iinclude iWeb-click idata ifor ibetter iUXi(User iexperience), iMedical irecords ifor ibetter iautomation iin ihealthcare, ibiological idata iand imany imore.

 Applications ithat icannot ibe iprogrammed: iThere iare isome itasks ithat icannot ibe iprogrammedias ithe icomputers iwe iuse iare inot imodelled ithat iway.iExamples iinclude iAutonomous iDriving, iRecognition itasks ifrom iunordered idata i(Face iRecognition/ iHandwriting iRecognition), iNatural ilanguage iProcessing, icomputer iVision ietc.

 Understanding iHuman iLearning: iThis iis ithe iclosest iwe ihave iunderstood iand imimicked ithe ihuman ibrain.iIt iis ithe istart iof ia inew irevolution, iThe ireal iAI.iNow, iAfter ia ibrief iinsight ilets icome ito ia imore iformal idefinition iof iMachine iLearning.

VII. CONCLUSION

(13)

International Journal of Modern Trends in Engineering and Research (IJMTER)

ifew iarticles ion iwhat iAI iis, iand ihow iit iwill iundoubtedly ichange ithe iworld. iThe itruth iof ithe imatter iis ithat ia iconsiderable ilot iof ithe ihuge igiants, iincluding iGoogle, iFacebook, iand ithe iinternet ibusiness idestinations, ifor iexample, iAmazon iand iAlibaba, iand iothers ihave ijust ibegun iexecuting iAI iand iML ifor itheir ibusiness.i

i i i i

iREFERENCES

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[2] ihttps://www.investopedia.com/terms/m/machine-learning.asp

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[4]ihttps://medium.com/@priyalwalpita/types-of-machine-learning-556529ad6a23

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[6]ihttps://dzone.com/articles/how-to-create-a-perfect-decision-tree

[7]ihttps://en.wikipedia.org/wiki/Machine_learning#Overview

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