Kuwanikwa kweVanotariswa Non-Linear Models
Munyika ine simba yekuongorora data, anotariswa mamodheru asina mutsara anomira sezvishandiso zvine simba uye zvinochinjika. Aya mamodheru, anopfuura echinyakare mitsetse tekinoroji, anoita kuti zvikwanise kugadzirisa nyaya dzakaoma nekurongeka kukuru. Kudzidziswa uku, kunowanikwa paOpenClassrooms, kunopa iwe wakasarudzika mukana wekuongorora aya epamusoro matekiniki.
Munguva yekudzidziswa uku, iwe unozounzwa kune dzakasiyana-siyana dzisiri-mutsara nzira, senge miti yesarudzo uye masango asina kurongeka. Aya matekiniki, anoshandiswa zvakanyanya mumunda wesainzi yedata, anozivikanwa nekugona kwavo kuenzanisira hukama hwakaoma pakati pezvinosiyana.
Simbiso inoiswa pakunzwisisa kunoshanda kweiyo pfungwa, nekudaro ichikubvumidza iwe kuti uishandise nemazvo mumapurojekiti ako emangwana. Nemaoko-pamaitiro ekudzidzisa, kudzidziswa uku kunokugadzirira iwe kuti uve nyanzvi mukushandiswa kweanotariswa asiri-mutsara modhi.
Nekuita mukudzidziswa uku, uri kutora hofori kusvetuka kusvika pakuwana hunyanzvi hunokosheswa zvakanyanya muindasitiri yanhasi yetekinoroji. Usapotsa mukana uyu kuti uzvitsaure mumunda wekuongorora data.
Wedzera Zivo Yako Yekuenzanisira
Muchikamu chinogara chichishanduka, zvakakosha kugona matekiniki ezvino. Iyi kosi inokutungamira iwe kuburikidza nemanuances eanotariswa asina mutsara modhi, achikubvumidza iwe kuti uwane yakadzama uye inoshanda nzwisiso yeaya akakosha maturusi.
Iwe unozotungamirwa kuti uongorore pfungwa dzepamberi senge tsigiro vector michina (SVM) uye neural network, ayo ari mainstays mumunda wekudzidza muchina. Aya matekiniki, anozivikanwa nekurongeka uye kuchinjika, zvinhu zvakakura mubhokisi rekushandisa chero nyanzvi yedata.
Kudzidziswa uku kunosimbisawo kukosha kwekuyambuka-kusimbisa uye hyperparameter optimization, matanho akakosha ekuona kuita uye kuvimbika kwemamodheru ako. Iwe unozodzidza kugona aya akaomesesa maitiro zviri nyore uye nekuvimba.
Pamusoro pezvo, iwe uchave nemukana wekudzidzira hunyanzvi hwako hutsva kuburikidza nemapurojekiti epasirese, zvichikubvumidza kuti ubatanidze ruzivo rwako uye uzvigadzirire kumatambudziko epasirese. Iyi yemaoko-on maitiro inovimbisa kuti hauzongokwanisa kunzwisisa aya pfungwa, asi zvakare kuishandisa nemazvo mumapurojekiti ako emangwana.
Tsvaga Advanced Modelling Techniques
Iyi mitoo, kunyange iri mberi, inoratidzwa nenzira yokuti inogona kusvikirika kunyange naavo vachiri vatsva mundima.
Simbiso inoiswawo pakukosha kwekuongororwa kwemuenzaniso uye kugadzirisa, matanho akakosha mukuona kuti ongororo yako ndeyechokwadi uye yakavimbika. Iwe unozodzidza kufambisa maitiro aya nekunzwisisa kwakajeka kwemisimboti, kukugadzirira iwe kuti ugone kubudirira mune ramangwana rako.
Sezvatotaurwa, kudzidziswa kunopa iwe mukana wekuita mapurojekiti anoshanda, achikubvumidza iwe kuita hunyanzvi hwawakawana mumamiriro chaiwo. Iyi maoko-pamaitiro haingogadzirire iwe kuti unzwisise pfungwa dzedzidziso, asi zvakare kudzishandisa nemazvo munyika yehunyanzvi.
Tora mukana uyu kuti uzvishongedze nehunyanzvi hwekukunda mundima inogara ichishanduka ye data analytics.
