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Hello everyone Assalamualaikum this is Shaqel. So I hope you all are doing well. Today's particular video was very important topic. Today's topic is about the model of Ractor Machine Learning.

In parallel we have to learn a particular mention ofchnic related to zarni and misija and the rc. There are Japanese people involved and there are also many people involved. We have a long learning experience related to register therefore Saturday we will have a regression problem. dhata aamlajatakurta hoi aqta artificial at intelligence kietkurta hoi jatakikurwe fiwchara help kurwe oi related data thaqla aamlajatke kikurwe fiwchara based on this data set aamlajatke notunecta value prediction kurwe tana.

tana. tana. amlajat jati train a test data talada kuri bakh rosbhila di shun kuri bakh jai kuri nai kano prothi ketra ki hoi. prothi ketra actually jatahoi aamla moolotatakurikki aamlajat amlajat train test speed de bakkuri bakkara pore aamlaa finali train data de train kura itis data de test kura ekur de kki aamlajat modela rakurikki kirokom jodhe aamlajat modela rakurikki bhalo asetalat kui bhalo aamlajat jodhe akurikki bhalo nashi shekatra nakikuri aamlajatke nai aamlajatke aamlajatke nai aamlajatke kuri haapar paramit tuning kuri aamlajat fiwchara help kare nai kano kintu aamlajatke kurta hoi jakun moonohabajat aamla aamlaa aamlaa mootamuti satisfy levela a masheda t Richa madi rakurikki atta quiki Cantu bhаз lakh amlauli tee, amlaiyや, majobtutven ut wind pipeline amlai ki pers endo aah wraqul meaning ve bata Gandhi cubita vithy re tha KIM różnych tranfoca 5x8Cara prasad reasonably price ones we will go too dA about the then our bb not hyper feature engineering related to the kajgul aase, seghul akura evan analysis kura.

So, kajgul akura pourre apna judhe mona jai ae algodum di ae our actor bhala akurvi sash se, so am ae te ki sep kurti sae. So, se te judhe hua e random forest, tal se te ki bhala apni sep kurven. Et amid aakati. So, apne dite pati saen jai ami actor prediction kurti 101, manegul ki featureer valu, to featureer valu amidisi, dA our pore ama ki output prediction kurti sae yes, ami ae te asla ki data set ba ki problem setan i ami aer details kota bolsi na.

So, whatever, ama actor prediction kurti sae yes, ami model dek sep kurti random forest, tal se mea, to random forest, tal se mea, forest junna ami model dek sep kurti, to ae te sep kura pourre, beki ama aakati kurti sae kota asri paa e pao bho te te dek ki prediction da ki aase asri na ki no asri paa kunuk chen jaase ki na, seta me dek go, te kase, Karon shatukata bolte asri ami ae model dek aakom jai prediction da dice, sep kura pourre autic sep prediction dae bek Karon hoti ae model dek ki ae model dek sep kura pourre thag be, ae intelligence dae sep kura pourre thag be, te kase. So, shate ki ae ae intelligence daak ama ki baabe sep kurti paa e te ama kata dek go. Okay, so dakhin ama anag bhabi kurti paa e ama cya lehgana pk lehk kurti paa e ama cya lehgana jable bhi kurti paa e wata bhaa ama aakpash te kata dakhin ae, takhore ae kki naame sep kurti chan fail da, sapo jama reita ho se te klasifikasi na te pourre problem, so ae reita naam dhich se, jai mai model, okay, so mai model 1 dhich se, tikase, mai model, mai model 1, tikase ae da ho se ke ama model ae nam, main ae kyi file da sep kura ro, ae file da ki naame sep kura ro, ae daame dhich mai model 1, deen ae nama ke mootta bolte thaabe, tikase ae asule, ae kane moot ho se, jable ami sep kura ro, to khan airmudo se w b, w maan ae kyi janan, w maan ae ho se, write binary, write k, write binary, tikase, to ae kane just ae as, sapo jati lama fail, then kubi simpil pk dot dump. dumper fyi to rai ama ke just ae bar da he, sorry ae bar da he, ae bar da he, ae bar da he, ae bar da he, ae kyi kurti paa e, ae, ae kyi kurti paa e, ae kyi jai algodama use kurti paa, ae algodama jae apnarki bolte kyeong.

Ae abhzakta se, ae abhzakta kae apnarki ke, ae kane liqta habe shor bolta bolta bolta bolta bolna, tikase say, tikase sos, sapo jai ami liq lam, airpor ae apnarki, ae liqta habe just ae, ae kane jai tu fail dei se, ae ae kane variable nam jai tu fail dei se, ae kane wa, ae kane wa fi el dei di chi, ae da pura da deen kura ka, apnarko ae kane wa, ae kane wa, ae kane wa, ae kane wa, ae kane wa,ūik iyidfind ai drbar es mutual apag Smash Crude r каждый parad juice 10 kg trouve silent versator working directory work directory problemonian file nom career 8 0 &5 ... 不 ... n darkest CSS tbc.dump vitho rr vitho rr rr tp kvm dl tp kvm tp kvm tp kvm tp kvm dl kvm tp kvm td kvm tp kvm vitho vitho kvm td nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd