Okuqukethwe[Fihla][Bonisa]
- I-1. I-Titanic
- 2. I-Irish Flower Classification
- 3. I-Boston House Price Prediction
- 4. Ukuhlolwa Kwekhwalithi Yewayini
- 5. Ukubikezela Kwemakethe Yamasheya
- 6. Isincomo se-Movie
- 7. Isibikezelo Sokufaneleka Kokulayisha
- 8. Ukuhlaziya Imizwa kusetshenziswa Idatha ye-Twitter
- 9. Ikusasa Sales Prediction
- 10. Ukutholwa Kwezindaba Okungelona iqiniso
- 11. Amakhuphoni Ukuthenga Ukubikezela
- 12. Customer Churn Prediction
- 13. I-Wallmart Sales Forecasting
- 14. Ukuhlaziywa Kwedatha ye-Uber
- 15. Ukuhlaziywa kwe-Covid-19
- Isiphetho
Ukufunda ngomshini kuyisifundo esilula sendlela yokufundisa uhlelo lwekhompiyutha noma i-algorithm ukuze uthuthuke kancane kancane emsebenzini othile owethulwe ezingeni eliphezulu. Ukuhlonzwa kwesithombe, ukutholwa kokukhwabanisa, amasistimu wokuncoma, nezinye izinhlelo zokusebenza zokufunda ngomshini sekuvele kufakazelwe njengokudumile.
Imisebenzi ye-ML yenza umsebenzi womuntu ube lula futhi usebenze kahle, yonga isikhathi futhi iqinisekise umphumela wekhwalithi ephezulu. Ngisho ne-Google, injini yokusesha edume kakhulu emhlabeni, iyayisebenzisa ukufunda imishini.
Kusukela ekuhlaziyeni umbuzo womsebenzisi nokushintsha umphumela ngokusekelwe emiphumeleni kuya ekuboniseni izihloko ezithrendayo nezikhangisi ngokuhlobene nombuzo, kunezinketho eziningi ezitholakalayo.
Ubuchwepheshe obubonayo kanye nokuzilungisa akukude esikhathini esizayo.
Enye yezindlela ezinhle kakhulu zokuqalisa iwukuba ubambe iqhaza futhi uklame iphrojekthi. Ngakho-ke, sihlanganise uhlu lwamaphrojekthi aphezulu ayi-15 okufunda ngemishini kwabaqalayo ukuze uqalise.
1. Titanic
Lokhu kuvame ukubhekwa njengomsebenzi omkhulu kakhulu nojabulisa kakhulu kunoma ubani onentshisekelo yokufunda kabanzi mayelana nokufunda komshini. Inselele ye-Titanic iphrojekthi yokufunda yomshini edumile futhi esebenza njengendlela enhle yokujwayela inkundla yesayensi yedatha ye-Kaggle. Isethi yedatha ye-Titanic yenziwe idatha yangempela kusukela ekushoneni komkhumbi obi.
Kuhlanganisa imininingwane efana neminyaka yomuntu, isimo senhlalakahle yezomnotho, ubulili, inombolo yegumbi, ichweba lokusuka, futhi, okubaluleke kakhulu, ukuthi wasinda yini!
Isu le-K-Nearest Neighbor kanye nesigaba sesihlahla sesinqumo kunqunywe ukukhiqiza imiphumela engcono kakhulu yale phrojekthi. Uma ufuna inselele yempelasonto esheshayo yokuthuthukisa eyakho Amakhono okufunda ngomshini, lena eku-Kaggle ingeyakho.
2. I-Irish Flower Classification
Abasaqalayo bathanda iphrojekthi yokuhlukanisa imbali ye-iris, futhi yindawo enhle yokuqala uma umusha ekufundeni ngomshini. Ubude bama-sepals namacembe buhlukanisa ukuqhakaza kwe-iris kwezinye izinhlobo. Inhloso yale phrojekthi ukuhlukanisa izimbali zibe izinhlobo ezintathu: i-Virginia, i-setosa, ne-Versicolor.
Ngokuzivocavoca kokuhlukanisa, iphrojekthi isebenzisa idathasethi yembali ye-Iris, esiza abafundi ekufundeni izisekelo zokubhekana namanani ezinombolo kanye nedatha. Isethi yedatha yembali ye-iris incane engagcinwa enkumbulweni ngaphandle kwesidingo sokukala.
3. I-Boston House Price Prediction
Omunye owaziwayo Idathasethi yabaqalayo ekufundeni komshini idatha ye-Boston Housing. Umgomo wayo ukubikezela amanani asekhaya ezindaweni ezahlukahlukene zaseBoston. Kuhlanganisa izibalo ezibalulekile ezifana nobudala, izinga lentela yendawo, izinga lobugebengu, ngisho nokuba seduze kwezikhungo zemisebenzi, konke okungase kuthinte intengo yezindlu.
Idathasethi ilula futhi incane, okwenza kube lula ukuhlola abaqalayo. Ukuthola ukuthi yiziphi izici ezithonya intengo yendawo e-Boston, amasu okubuyisela emuva asetshenziswa kakhulu kumapharamitha ahlukahlukene. Yindawo enhle yokuzijwayeza amasu okubuyisela emuva nokuhlola ukuthi asebenza kahle kangakanani.
4. Ukuhlolwa Kwekhwalithi Yewayini
Iwayini yisiphuzo esidakayo esingajwayelekile esidinga iminyaka yokubila. Ngenxa yalokho, ibhodlela lakudala lewayini liyiwayini elibizayo nelisezingeni eliphezulu. Ukukhetha ibhodlela elifanele lewayini kudinga iminyaka yolwazi lokunambitha iwayini, futhi kungaba yinqubo yokushaya noma yokuphuthelwa.
Iphrojekthi yokuhlola ikhwalithi yewayini ihlola amawayini kusetshenziswa izivivinyo ze-physicochemical ezifana nezinga lotshwala, i-asidi engaguquki, ukuminyana, i-pH, nezinye izici. Le phrojekthi futhi inquma izinga lekhwalithi nenani lewayini. Ngenxa yalokho, ukuthenga iwayini kuba mnandi.
5. Isibikezelo Semakethe Yamasheya
Lesi sinyathelo siyamangaza ukuthi usebenza noma cha emkhakheni wezezimali. Idatha yemakethe yesitoko ifundwa kakhulu yizifundiswa, amabhizinisi, futhi njengomthombo wemali engenayo yesibili. Ikhono lososayensi wedatha lokufunda nokuhlola idatha yochungechunge lwesikhathi nalo libalulekile. Idatha evela emakethe yamasheya iyindawo enhle yokuqala.
Ingqikithi yalo mzamo ukubikezela inani lesikhathi esizayo lesitoko. Lokhu kusekelwe ekusebenzeni kwezimakethe zamanje kanye nezibalo zeminyaka edlule. U-Kaggle ubelokhu eqoqa idatha kunkomba ye-NIFTY-50 kusukela ngo-2000, futhi okwamanje ibuyekezwa masonto onke. Kusukela ngomhlaka-1 Januwari 2000, ibiqukethe amanani esitoko ezinhlangano ezingaphezu kuka-50.
6. Isincomo se-Movie
Ngiyaqiniseka ukuthi uke waba nalowo muzwa ngemva kokubona ifilimu emnandi. Ingabe uke wazizwa ushukumiseleka ukuba unyakazise izinzwa zakho ngokuzitika ngokubukela amafilimu afanayo?
Siyazi ukuthi izinsiza ze-OTT ezifana ne-Netflix zithuthukise izinhlelo zazo zokuncoma kakhulu. Njengomfundi ofunda umshini, uzodinga ukuqonda ukuthi ama-algorithms anjalo aqondisa kanjani amaklayenti ngokusekelwe kulokho akuthandayo nezibuyekezo.
Idatha ye-IMDB esethwe ku-Kaggle cishe ingelinye lamamodeli aphelele kakhulu, avumela amamodeli wokuncoma ukuthi aqondiswe ngokusekelwe esihlokweni se-movie, isilinganiso sekhasimende, uhlobo, nezinye izici. Futhi kuyindlela enhle kakhulu yokufunda mayelana nokuhlunga Okusekelwe Kokuqukethwe kanye Nobunjiniyela Besici.
7. Layisha Ukubikezela Kokufaneleka
Umhlaba uzungeza emalimboleko. Umthombo omkhulu wamabhange wenzuzo uvela kwinzalo yemali mboleko. Ngakho bayibhizinisi labo eliyisisekelo.
Umuntu ngamunye noma amaqembu abantu angakhulisa umnotho kuphela ngokutshala imali enkampanini ngethemba lokuyibona ikhuphuka inani esikhathini esizayo. Kwesinye isikhathi kubalulekile ukufuna imali ebolekiwe ukuze ukwazi ukuzifaka engozini yalolu hlobo futhi uhlanganyele enjabulweni ethile yezwe.
Ngaphambi kokuthi imalimboleko yamukelwe, amabhange avamise ukuba nenqubo eqinile okufanele ayilandele. Njengoba imali mboleko iyisici esibaluleke kangaka empilweni yabantu abaningi, ukubikezela ukufaneleka kokubolekwa okwenziwa othile kungaba yinzuzo enkulu, okuvumela ukuhlela okungcono ngale kokwamukelwa noma ukwenqatshwa kwemali mboleko.
8. Ukuhlaziywa Kwemizwa kusetshenziswa Idatha ye-Twitter
Sibonga u amanethiwekhi ezokuxhum njenge-Twitter, i-Facebook, ne-Reddit, imibono eyengeziwe kanye nezitayela zibe lula kakhulu. Lolu lwazi lusetshenziselwa ukususa imibono ngemicimbi, abantu, ezemidlalo, nezinye izihloko. Imibono yezinhlelo zokufunda ngemishini ehlobene nezimayini zisetshenziswa kuzilungiselelo ezahlukahlukene, okuhlanganisa imikhankaso yezepolitiki nokuhlolwa kwemikhiqizo ye-Amazon.
Le phrojekthi izobukeka inhle kuphothifoliyo yakho! Ukuze uthole imizwa nokuhlaziya okusekelwe kusici, amasu afana nemishini yokusekela i-vector, ukuhlehla, nama-algorithms okuhlukanisa angasetshenziswa kakhulu (ukuthola amaqiniso nemibono).
9. Ikusasa Sales Prediction
Amabhizinisi amakhulu e-B2C nabathengisi bafuna ukwazi ukuthi umkhiqizo ngamunye osohlwini lwabo lwempahla uzothengisa malini. Izibikezelo zokuthengisa zisiza abanikazi bamabhizinisi ekunqumeni ukuthi yiziphi izinto ezidingeka kakhulu. Ukubikezela okunembayo kokuthengisa kuzokwehlisa kakhulu ukumoshwa ngenkathi kuphinde kunqume umthelela okhulayo kubhajethi zesikhathi esizayo.
Abathengisi abafana ne-Walmart, i-IKEA, i-Big Basket, ne-Big Bazaar basebenzisa isibikezelo sokuthengisa ukuze balinganisele isidingo somkhiqizo. Kufanele ujwayelane namasu ahlukahlukene okuhlanza idatha eluhlaza ukuze wakhe amaphrojekthi anjalo e-ML. Futhi, ukuqonda kahle ukuhlaziywa kokuhlehla, ikakhulukazi ukuhlehla komugqa, kuyadingeka.
Kulezi zinhlobo zemisebenzi, uzodinga ukusebenzisa imitapo yolwazi efana ne-Dora, i-Scrubadub, i-Pandas, i-NumPy, neminye.
10. Ukutholwa Kwezindaba Okungelona iqiniso
Omunye umzamo wokufunda womshini osezingeni eliphezulu ohloselwe izingane zesikole. Izindaba ezingamanga zibhebhetheka njengomlilo wequbula, njengoba sazi sonke. Konke kuyatholakala ezinkundleni zokuxhumana, kusukela ekuxhumeni abantu ngabanye kuya ekufundeni izindaba zansuku zonke.
Ngenxa yalokho, ukuthola izindaba ezingamanga kuye kwaba nzima kakhulu kulezi zinsuku. Izinkundla zokuxhumana eziningi ezinkulu zokuxhumana, njenge-Facebook ne-Twitter, sezivele zinawo ama-algorithms okuthola izindaba mbumbulu ekuthunyelweni nasekuphakeleni.
Ukuze uhlonze izindaba ezingamanga, lolu hlobo lwephrojekthi ye-ML ludinga ukuqonda okuphelele kwezindlela eziningi ze-NLP nama-algorithms okuhlukanisa (PassiveAggressiveClassifier noma i-Naive Bayes classifier).
11. Amakhuphoni Ukuthenga Ukubikezela
Amakhasimende aya ngokuya ecabanga ukuthenga ku-inthanethi ngenkathi i-coronavirus ihlasela umhlaba ngo-2020. Ngenxa yalokho, izitolo ziphoqelekile ukuthi zishintshe amabhizinisi azo ku-inthanethi.
Amakhasimende, ngakolunye uhlangothi, asafuna izipesheli ezinhle, njengoba abenjalo ezitolo, futhi aya ngokuya azingela amakhuphoni awonga kakhulu. Kukhona namawebhusayithi anikezelwe ukudala amakhuphoni kumakhasimende anjalo. Ungafunda mayelana nokumbiwa kwedatha ekufundeni komshini, ukukhiqiza amagrafu ebha, amashadi ophaya, nama-histograms ukuze ubone ngeso lengqondo idatha, futhi ufake ubunjiniyela ngale phrojekthi.
Ukuze ukhiqize izibikezelo, ungabheka nezindlela zokusebenzisa idatha zokuphatha amanani e-NA kanye nokufana kwe-cosine kokuguquguqukayo.
12. I-Customer Churn Prediction
Abathengi bayimpahla ebaluleke kakhulu yenkampani, futhi ukuyigcina kubalulekile kunoma yiliphi ibhizinisi elihlose ukukhulisa imali engenayo kanye nokwakha ukuxhumana okunenjongo kwesikhathi eside nabo.
Ngaphezu kwalokho, izindleko zokuthola iklayenti elisha ziphakeme ngokuphindwe kahlanu kunezindleko zokusekela elikhona kakade. I-Customer Churn/Attrition inkinga yebhizinisi eyaziwa kakhulu lapho amakhasimende noma ababhalisile beyeka khona ukwenza ibhizinisi nesevisi noma inkampani.
Ngeke besaba ikhasimende elikhokhayo. Ikhasimende lithathwa njengelixakile uma sekuyinani elithile lesikhathi kusukela ikhasimende lagcina ukuxhumana nenkampani. Ukubona ukuthi iklayenti lizoxova yini, kanye nokunikeza ngokushesha ulwazi olufanele oluhloselwe ukugcinwa kwamakhasimende, kubalulekile ekwehliseni ukuxova.
Ubuchopho bethu abukwazi ukulindela inzuzo yamakhasimende ezigidini zamaklayenti; lapha kulapho ukufunda ngomshini kungasiza khona.
13. I-Wallmart Sales Forecasting
Esinye sezicelo ezivelele kakhulu zokufunda komshini ukubikezela ukuthengisa, okubandakanya ukuthola izici ezithonya ukuthengiswa komkhiqizo nokulindela umthamo wokuthengisa esikhathini esizayo.
Idathasethi ye-Walmart, equkethe idatha yokuthengisa evela ezindaweni ezingu-45, isetshenziswa kulolu cwaningo lokufunda ngomshini. Ukuthengisa ngesitolo ngasinye, ngesigaba, njalo ngeviki kufakiwe kudathasethi. Injongo yale phrojekthi yokufunda ngomshini iwukulindela ukuthengiswa komnyango ngamunye endaweni ngayinye ukuze bakwazi ukwenza kangcono isiteshi esiqhutshwa idatha kanye nezinqumo zokuhlela uhlu.
Ukusebenza nedathasethi ye-Walmart kunzima njengoba iqukethe imicimbi ekhethiwe yokubeka phansi enomthelela ekuthengisweni futhi kufanele kucatshangelwe.
14. Ukuhlaziywa Kwedatha ye-Uber
Uma kuziwa ekusebenziseni nasekuhlanganiseni ukufunda komshini nokufunda okujulile ezinhlelweni zabo zokusebenza, isevisi edumile yokwabelana ngokugibela ayisemuva kakhulu. Njalo ngonyaka, icubungula izigidigidi zohambo, ivumela abagibeli ukuthi bahambe nganoma yisiphi isikhathi emini noma ebusuku.
Ngenxa yokuthi inesisekelo samaklayenti esikhulu kangaka, idinga isevisi yamakhasimende ehlukile ukuze ibhekane nezikhalazo zabathengi ngokushesha okukhulu.
I-Uber inedathasethi yezigidi zokulanda engayisebenzisa ukuze ihlaziye futhi ibonise uhambo lwamakhasimende ukuze iveze imininingwane futhi ithuthukise ulwazi lwamakhasimende.
15. Ukuhlaziywa kwe-Covid-19
I-COVID-19 ishanele imbulunga yonke namuhla, hhayi nje ngomqondo wobhubhane. Ngenkathi ochwepheshe bezokwelapha begxile ekukhiqizeni imigomo esebenzayo kanye nokugoma umhlaba, ososayensi bemininingwane abekho kude.
Amacala amasha, ukubalwa kwansuku zonke, ukufa kwabantu, nezibalo zokuhlola konke kwenziwa esidlangalaleni. Izibikezelo zenziwa nsuku zonke ngokusekelwe ekuqubukeni kwe-SARS kwekhulunyaka eledlule. Ukuze wenze lokhu, ungasebenzisa ukuhlaziywa kokuhlehla futhi usekele amamodeli wokubikezela asekelwe emshinini we-vector.
Isiphetho
Ukufingqa, sixoxe ngamanye amaphrojekthi aphezulu e-ML azokusiza ekuhloleni uhlelo lokufunda ngomshini kanye nokubamba imibono nokusebenzisa kwayo. Ukwazi ukuthi ukuhlanganisa kanjani Ukufunda Ngomshini kungakusiza ukuthi uthuthuke emsebenzini wakho njengoba ubuchwepheshe buthatha izintambo kuyo yonke imboni.
Ngenkathi ufunda Ukufunda Ngomshini, sincoma ukuthi uzilolonge imiqondo yakho futhi ubhale wonke ama-algorithms akho. Ukubhala ama-algorithms ngenkathi ufunda kubaluleke kakhulu kunokwenza iphrojekthi, futhi kukunikeza inzuzo ekuqondeni izifundo ngendlela efanele.
shiya impendulo