Isiqulatho[Fihla][Bonisa]
- 1. Ititanic
- 2. Ukuhlelwa kweentyatyambo zaseIrish
- 3. Boston House Price Prediction
- 4. Uvavanyo lomgangatho weWayini
- 5. Ukuqikelelwa kweMarike yeStock
- 6. Isindululo seMovie
- 7. Ukuqikelelwa kokufaneleka komthwalo
- 8. Uhlalutyo lweemvakalelo usebenzisa iDatha ye-Twitter
- 9. I-Future Sales Prediction
- 10. Ukufunyanwa kweNdaba ezingeyonyani
- 11. Iikhuphoni zokuThenga Ukuqikelela
- 12. Customer Churn Prediction
- 13. Uqikelelo lwentengiso yeWallmart
- 14. Uhlalutyo lweDatha ka-Uber
- 15. Uhlalutyo lwe-Covid-19
- isiphelo
Ukufunda ngomatshini sisifundo esilula sendlela yokufundisa inkqubo yekhompyutheni okanye i-algorithm yokuphucula ngokuthe ngcembe kumsebenzi othile owenziwe kwinqanaba eliphezulu. Ukuchongwa kwemifanekiso, ukubhaqwa kobuqhophololo, iinkqubo zokucebisa, kunye nezinye iinkqubo zokufunda koomatshini sele zingqineke zidumile.
Imisebenzi ye-ML yenza umsebenzi wabantu ube lula kwaye usebenze kakuhle, ukonga ixesha kunye nokuqinisekisa iziphumo ezikumgangatho ophezulu. Nditsho noGoogle, eyona njini yokukhangela idumileyo kwihlabathi, uyayisebenzisa yokufunda umatshini.
Ukusuka ekuhlalutyeni umbuzo womsebenzisi kunye nokuguqula iziphumo ezisekelwe kwiziphumo ukubonisa izihloko ezihamba phambili kunye neentengiso ngokunxulumene nombuzo, kukho iindlela ezahlukeneyo zokukhetha ezikhoyo.
Itekhnoloji enengqiqo kunye nokuzilungisa ayikude kwixesha elizayo.
Enye yeendlela eziphambili zokuqalisa kukufumana izandla kunye noyilo lweprojekthi. Ke ngoko, siqulunqe uluhlu lweeprojekthi ezili-15 eziphezulu zokufunda koomatshini zabaqalayo ukuze uqalise.
1. Titanic
Oku kuhlala kuthathwa njengowona msebenzi mkhulu kwaye uyonwabisa kuye nabani na onomdla wokufunda ngakumbi malunga nokufunda koomatshini. Umceli mngeni weTitanic yiprojekthi yokufunda koomatshini edumileyo ekwasebenza njengendlela elungileyo yokuqhelana neqonga lesayensi yedatha yeKaggle. Isethi yedatha ye-Titanic yenziwe yidatha yokwenyani ukusuka ekuzikeni kwenqanawa engalunganga.
Ibandakanya iinkcukacha ezinje ngobudala bomntu, ubume bentlalo-ntle, isini, inombolo yekhabhinethi, izibuko lokusuka, kwaye, okona kubaluleke kakhulu, nokuba basindile na!
Ubuchule boBumelwane be-K kunye nomhluli womthi wesigqibo baye bazimisela ukuvelisa ezona ziphumo zingcono zale projekthi. Ukuba ujonge umceli mngeni okhawulezayo wempelaveki ukuphucula owakho Izakhono zokuFunda ngoomatshini, le iseKaggle yeyakho.
2. Irish Flower Classification
Abaqalayo bayayithanda iprojekthi yokuhlelwa kweentyatyambo ze-iris, kwaye yindawo entle ukuqala ukuba umtsha ekufundeni ngomatshini. Ubude be-sepals kunye neepetali ziyahlula iintyatyambo ze-iris kwezinye iintlobo. Injongo yale projekthi kukwahlula iintyatyambo zibe ziindidi ezintathu: iVirginia, i-setosa, kunye ne-Versicolor.
Kwimisebenzi yokuhlela, iprojekthi isebenzisa isethi yedatha yentyatyambo ye-Iris, enceda abafundi ekufundeni iziseko zokujongana neenqobo zamanani kunye neenkcukacha. Iiseti yentyatyambo ye-iris incinci enokugcinwa kwinkumbulo ngaphandle kwesidingo sokukala.
3. Boston House Price Prediction
Omnye owaziwayo isethi yedatha yabaqalayo ekufundeni koomatshini yidatha yeZindlu zaseBoston. Injongo yayo kukuqikelela amaxabiso asekhaya kwiindawo ezahlukeneyo zaseBoston. Iquka iinkcukacha-manani ezibalulekileyo ezifana nobudala, izinga lerhafu yepropathi, izinga lolwaphulo-mthetho, kwanokusondelelana namaziko emisebenzi, konke oku kunokuchaphazela amaxabiso ezindlu.
Iseti yedatha ilula kwaye incinci, iyenza ukuba kube lula ukuyilinga kwi-novices. Ukuqonda ukuba zeziphi izinto ezinefuthe kwixabiso lepropathi eBoston, iindlela zokubuyisela umva zisetyenziswa kakhulu kwiiparamitha ezahlukeneyo. Yindawo entle yokuziqhelanisa neendlela zokubuyisela umva kwaye uvavanye ukuba zisebenza kakuhle kangakanani na.
4. Uvavanyo Lomgangatho Wewayini
Iwayini sisiselo esinxilisayo esingaqhelekanga esifuna iminyaka yokubilisa. Ngenxa yoko, ibhotile ye-antique yewayini yiwayini enexabiso eliphezulu kunye nekhwalithi ephezulu. Ukukhetha ibhotile eyiyo yewayini kufuna iminyaka yolwazi lokungcamla iwayini, kwaye inokuba yinkqubo yokubetha okanye yokuphoswa.
Iprojekthi yovavanyo lomgangatho wewayini ivavanya iwayini kusetyenziswa iimvavanyo zephysicochemical ezifana nenqanaba lotywala, iasidi esisigxina, ubuninzi, i-pH, kunye nezinye izinto. Le projekthi ikwamisela imilinganiselo yomgangatho wewayini kunye nobungakanani. Ngenxa yoko, ukuthenga iwayini kuba yimpepho.
5. Uqikelelo lweMarike yeStock
Eli nyathelo linika umdla wokuba ngaba usebenza okanye awusebenzi kwicandelo lezemali. Idatha ye-stock market ifundwa ngokubanzi ngabafundi, amashishini, kwaye njengomthombo wengeniso yesibini. Isakhono senzululwazi yedatha yokufunda kunye nokuphonononga idatha yoluhlu lwexesha sikwabalulekile. Idatha evela kwi-stock market yindawo enhle yokuqala.
Undoqo womzamo kukuqikelela ixabiso lexesha elizayo lesitokhwe. Oku kusekwe kwindlela esebenza ngayo imarike yangoku kunye neenkcukacha-manani kwiminyaka engaphambili. I-Kaggle iqokelele idatha kwi-NIFTY-50 index ukususela ngo-2000, kwaye ngoku ihlaziywa ngeveki. Ukususela ngomhla we-1 kaJanuwari 2000, iqulethe amaxabiso esitokhwe kwimibutho engaphezu kwama-50.
6. ISincomo seMovie
Ndiqinisekile ukuba uye waziva emva kokubona imuvi emnandi. Ngaba ukhe waziva unomdla wokuchukumisa iimvakalelo zakho ngokubukela iifilimu ezifanayo?
Siyazi ukuba iinkonzo ze-OTT ezifana neNetflix ziphucule iinkqubo zabo zokucebisa kakhulu. Njengomfundi ofunda ngomatshini, kuya kufuneka uqonde ukuba i-algorithms enjalo ijolise njani kubathengi ngokusekwe kwizinto abazithandayo kunye nophononongo.
Iseti yedatha ye-IMDB kwiKaggle isenokuba yeyona igqibelele, ivumela imodeli yengcebiso ukuba iqikelelwe ngokusekwe kwisihloko somboniso bhanyabhanya, ukukala kwabathengi, uhlobo, kunye nezinye izinto. Ikwayeyona ndlela igqwesileyo yokufunda malunga noHluzo oluSekwe kuMxholo kunye nobuNjineli boMxholo.
7. Layisha Ukufaneleka Uqikelelo
Ihlabathi lijikeleza kwimali-mboleko. Owona mthombo wengeniso yebhanki uvela kwinzala kwiimali-mboleko. Yiyo loo nto zishishini labo elisisiseko.
Abantu okanye amaqela abantu banokwandisa uqoqosho kuphela ngokutyala imali kwifemu ngethemba lokuyibona inyuka ixabiso kwixesha elizayo. Ngamanye amaxesha kubalulekile ukufuna imali-mboleko ukuze ukwazi ukuthatha umngcipheko wolu hlobo kwaye uthabathe inxaxheba kwiziyolo ezithile zehlabathi.
Phambi kokuba imali-mboleko yamkelwe, iibhanki zidla ngokuba nenkqubo engqongqo emayilandelwe. Njengoko imali-mboleko ingumba obaluleke kangaka kubomi babantu abaninzi, ukuqikelela ukufaneleka kwimali-mboleko umntu ayenzela isicelo kuya kuba luncedo kakhulu, ukuvumela ucwangciso olungcono ngaphaya kwemali-mboleko yamkelwe okanye yaliwe.
8. Uhlalutyo lweemvakalelo usebenzisa iDatha ye-Twitter
Enkosi Ku uthungelwano lweendaba zentlalo njenge-Twitter, Facebook, kunye neReddit, izimvo ezongezelelekileyo kunye neentsingiselo ziye zalula kakhulu. Olu lwazi lusetyenziselwa ukuphelisa izimvo ngeziganeko, abantu, imidlalo, kunye nezinye izihloko. Uluvo lwamanyathelo okufunda ngomatshini anxulumene nemigodi asetyenziswa kwiindawo ezahlukeneyo, kubandakanywa amaphulo ezopolitiko kunye novavanyo lwemveliso ye-Amazon.
Le projekthi iya kujongeka intle kwipotfoliyo yakho! Ukufumanisa imvakalelo kunye nohlalutyo olusekelwe kwinkalo, ubuchule obufana noomatshini be-vector yenkxaso, ukuhlehla, kunye nokuhlelwa kwe-algorithms kunokusetyenziswa ngokubanzi (ukufumana iinyani kunye nezimvo).
9. Ikamva Sales Prediction
Amashishini amakhulu e-B2C kunye nabarhwebi bafuna ukwazi ukuba yimalini imveliso nganye kwi-inventri yabo iya kuthengisa. Uqikelelo lwentengiselwano lunceda abanini bamashishini ekumiseleni ukuba zeziphi izinto ezikwimfuno ephezulu. Uqikelelo oluchanekileyo lwentengiso luya kunciphisa kakhulu inkcitho ngelixa lukwamisela impembelelo eyongeziweyo kuhlahlo lwabiwo-mali lwexesha elizayo.
Abathengisi abanje ngeWalmart, IKEA, Big Basket, kunye neBig Bazaar basebenzisa uqikelelo lwentengiso ukuqikelela imfuno yemveliso. Kuya kufuneka uqhelane neendlela ezahlukeneyo zokucoca idatha ekrwada ukuze wakhe iiprojekthi zeML. Kwakhona, ukubamba kakuhle uhlahlelo lokubuyela umva, ngakumbi uhlengahlengiso olulula lomgca, luyafuneka.
Kwezi ntlobo zemisebenzi, kuya kufuneka uqeshe amathala eencwadi afana neDora, Scrubadub, Pandas, NumPy, kunye nezinye.
10. Ukufunyanwa kweendaba zobuxoki
Ngomnye umzamo wokufunda womatshini ojonge phambili ojolise kubantwana besikolo. Iindaba zomgunyathi zisasazeka okomlilo wedobo, njengoko sisazi sonke. Yonke into iyafumaneka kumajelo eendaba ezentlalo, ukusuka ekudibaniseni abantu ukuya ekufundeni iindaba zemihla ngemihla.
Ngenxa yoko, ukufumana iindaba zobuxoki kuye kwaba nzima ngakumbi kule mihla. Iinethiwekhi ezininzi ezinkulu zeendaba zoluntu, ezinje ngoFacebook kunye ne-Twitter, sele zine-algorithms endaweni yokufumanisa iindaba ezingeyonyani ekuthunyelweni nasekuphakeleni.
Ukuchonga iindaba zobuxoki, olu hlobo lweprojekthi ye-ML ludinga ukuqonda ngokucokisekileyo iindlela ezininzi ze-NLP kunye ne-algorithms yokuhlelwa (PassiveAggressiveClassifier okanye i-Naive Bayes classifier).
11. Iikhuphoni Purchase Prediction
Abathengi baya becinga ngokuthenga kwi-intanethi xa i-coronavirus ihlasele iplanethi ngo-2020. Ngenxa yoko, iindawo zokuthenga ziye zanyanzeliswa ukuba zitshintshe ishishini labo kwi-intanethi.
Abathengi, kwelinye icala, basafuna izibonelelo ezinkulu, kanye njengoko babezivenkile, kwaye baya besanda ukuzingela iikhuphoni ezonga kakhulu. Kukho iiwebhusayithi ezinikezelwe ekudaleni amakhuphoni kubathengi abanjalo. Unokufunda malunga nokumbiwa kwedatha ekufundeni koomatshini, ukuvelisa iigrafu zebar, iitshathi zephayi, kunye ne-histograms ukujonga idatha, kunye nobunjineli obubonakalayo kule projekthi.
Ukuvelisa uqikelelo, unokujonga kwiindlela zokubethelwa kwedatha zokulawula ixabiso le-NA kunye nokufana kwe-cosine kwezinto eziguquguqukayo.
12. Customer Churn Prediction
Abathengi yeyona asethi ibalulekileyo yenkampani, kwaye ukuzigcina kubalulekile kulo naliphi na ishishini elijolise ekunyuseni ingeniso kunye nokwakha unxibelelwano olunentsingiselo yexesha elide kunye nabo.
Ngaphaya koko, iindleko zokufumana umxhasi omtsha ziphezulu ngokuphindwe kahlanu kuneendleko zokugcina lowo ukhoyo. Customer Churn / Attrition yingxaki yeshishini eyaziwayo apho abathengi okanye ababhalisi bayeka ukwenza ishishini ngenkonzo okanye inkampani.
Ngokufanelekileyo abasayi kuba ngumthengi ohlawulayo. Umthengi uthathwa njenge-churned ukuba ibilixesha elithile ukusukela oko umthengi wagqibela ukusebenzisana nenkampani. Ukuchonga ukuba ngaba umxhasi uya kuphazamiseka, kunye nokunika ngokukhawuleza ulwazi olufanelekileyo olujoliswe ekugcinweni kwabathengi, kubalulekile ekwehliseni i-churn.
Ubuchopho bethu abukwazi ukulindela ingeniso yabathengi kwizigidi zabathengi; Apha kulapho ukufunda koomatshini kunganceda khona.
13. Uqikelelo lweNtengiso lweWallmart
Esinye sezona zicelo zibalaseleyo zokufunda koomatshini kuqikelelo lwentengiso, olubandakanya ukufumanisa iimpawu eziphembelela ukuthengiswa kwemveliso kunye nokulindela umthamo wokuthengisa kwixesha elizayo.
I-dataset ye-Walmart, equlethe idatha yokuthengisa esuka kwiindawo ze-45, isetyenziswe kwesi sifundo sokufunda ngomatshini. Ukuthengiswa kwevenkile nganye, ngokwecandelo, ngeveki kufakwe kwidathasethi. Injongo yale projekthi yokufunda umatshini kukulindela ukuthengiswa kwesebe ngalinye kwindawo nganye ukuze bakwazi ukwenza ngcono ishaneli eqhutywa yidatha kunye nezigqibo zokucwangcisa uluhlu.
Ukusebenza kunye nedatha yeWalmart kunzima kuba iqulethe iziganeko ezikhethiweyo zokuphawula ezinempembelelo kwiintengiso kwaye kufuneka ziqwalaselwe.
14. Uhlalutyo lweDatha ka-Uber
Xa kufikwa ekuphumezeni nasekudibaniseni ukufunda koomatshini kunye nokufunda nzulu kwii-apps zabo, inkonzo ethandwayo yokwabelana ngokukhwela ayikude ngasemva. Nyaka ngamnye, iqhuba iibhiliyoni zohambo, ivumela abakhweli ukuba bahambe nangaliphi na ixesha emini okanye ebusuku.
Ngenxa yokuba inesiseko esikhulu somthengi, idinga inkonzo yomthengi ekhethekileyo ukujongana nezikhalazo zabathengi ngokukhawuleza.
U-Uber uneseti yedatha yezigidi zokuchola enokuzisebenzisa ukuhlalutya nokubonisa iihambo zabathengi ukufumanisa ulwazi kunye nokuphucula amava abathengi.
15. Uhlalutyo lwe-Covid-19
I-COVID-19 igqugqise ihlabathi namhlanje, hayi nje ngengqiqo yobhubhane. Ngelixa iingcali zonyango zigxile ekuveliseni ugonyo olusebenzayo kunye nokugonya umhlaba, izazinzulu zedatha abakho ngasemva.
Amatyala amatsha, ubalo olusebenzayo lwemihla ngemihla, ukubhubha kwabantu, kunye nezibalo zovavanyo zonke zenziwa esidlangalaleni. Uqikelelo lwenziwa yonke imihla ngokusekwe kuqhambuko lwe-SARS lwenkulungwane edlulileyo. Kule nto, ungasebenzisa uhlalutyo lokuhlehla kunye nenkxaso yeemodeli zokuqikelela ezisekwe kumatshini we-vector.
isiphelo
Ukushwankathela, sixoxe ngezinye zeeprojekthi eziphezulu zeML eziza kukunceda ekuvavanyeni inkqubo yokuFunda ngoMatshini kunye nokubamba iimbono kunye nokuphunyezwa kwayo. Ukwazi ukuba ungadibanisa njani ukuFunda koomatshini kunokukunceda ukuba uqhubele phambili nomsebenzi wakho njengoko itekhnoloji ithatha indawo kuwo onke amashishini.
Ngelixa ufunda ukuFunda ngoomatshini, sicebisa ukuba uziqhelanise neekhonsepthi zakho kwaye ubhale zonke ii-algorithms zakho. Ukubhala ii-algorithms ngelixa ufunda kubaluleke ngaphezu kokwenza iprojekthi, kwaye kukubonelela ngenzuzo ekuqondeni izifundo ngokufanelekileyo.
Shiya iMpendulo