Use This Way Of Training Machine Learning Models For Efficiency

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[@krishnaik06]

Check our courses
TechNeuron: 200+courses Lifetime Warranty
Course url: ineuron.ai/one-neuron
Full Stack Data Scientist Bootcamp 2.0 With Job Guaranteed: ineuron.ai/course/Full-Stack-Data-Science-BootCamp-2.0
Use Krish20 coupon code for 20% off

[@varavindhkumar4484]

Sir thankyou so much for you and the whole ineuron team for reducing the price of one neuron. I know the offer is 1ly for a week but also u guys have did it sir. I have buyed oneneuron and very happy sir, dont know how to express it. Love you sir.

[@ganeshsubramanian6217]

sir, thank you. Friday - you said it will be NLP live session.. I was waiting for it. When is the next session?

[@vigkan9610]

Hello @krish Naik the code krish20 is not working for data science bootcamp

[@AayushOstwal]

Machine Learning is not deterministic, Experiment is the only key!!! Thanks for such content... Kudos!!

[@yusufhenry9365]

You are truly a leader!!
What more can we ask for?
Thanks my able professor!!

[@bhabanisankardash7387]

Awesome ....Doing a grt job....hats off

[@rishichaitanya3081]

For so many days, one thing is running in my mind that like how can we decide the range to take for hyperparameter tuning.

[@folashadeolaitan6222]

This is amazing! Kudos! Do you mind uploading the notebook please? Thank you

[@dominickipronoh4324]

you gave me a very good idea in this video thanks so much Krish

[@bhuvekkumar4358]

Thank you sir for this amazing code

[@aswiniprakash2567]

Thanks dude, learnt a lot from ur videos 1k crossed pls upload end to end process video

[@user-cm7el2ju1s]

Good video , Thank you @Krish naik

[@androiduser457]

Would you mind include the eda, feature engineering, better use any boosting model with cv and everything inside pipeline? Thank you sir.

[@pratikpratik8495]

Tree algo are not sensitive towards un-scaled features, outlier while LR and other parameteric algo are sensitive towards un scaled feature, outlier. In that case this technique is not useful as you are using the same features for all algo

[@michaelomosebi6374]

I like this.
@Krish what do you say?

[@santino8675]

Correct

[@sparkersclubesports5743]

@pratik his point here is to use this approach of coding to train multiple models for selection of a good performing model out of that. Ofcourse u can run this code twice for scaled and unscaled data twice with corresponding models. AGREE?

[@joshuarexmondnunoootoo5316]

Same question I wanted to ask

[@yousuf1sg]

Respected Krish Naik Sir, You have to own your committed words in video that you will share the code after 1000 likes. Now more than 1200 and we are still waitig. Thanks

[@datamonster3212]

I just use LightGBM these days. It usually performs best and requires minimal data preprocessing.

[@msherazkhan7336]

Sir... Please upload detail video about tranform models... VIT, POLYVIT, AST... IF AST, its very well becaus my project to use AST Model for sound classification(.wave files)... Thanks

[@dominickipronoh4324]

Krish coul you show us how to compare the training and loss accuracies of deep learning models ie to compare new method with existing SOTA methods

[@AIWALABRO]

superb..!

[@rkjyoti4167]

superb.

[@ravindarmadishetty736]

Hi Krish, do you have any plans for Time Series forecasting?

[@nujhatfatma3476]

Sir please share and make a video on this project....

[@user-gh7pj3wz2j]

How can I increase the recall?? Even after hyperparameter tuning I was able to increase the recall for class 1 from 8 to 19..

[@marcellodichiera]

Sorry if I am asking something non sense. But instead of doing all of that, why not using AutoML libraries such as Pycaret?

[@sivachaitanya6330]

sir.......'LogisticRegression' object has no attribute 'accuracy_score' Error how did you get accuracyscore for logistic regresion?

[@ankitadeshpande2447]

Kirsh sir please teach also about machine learning model deployment in detail

[@Eswar.]

I strongly recommend you to look out campusx (nitish sir) videos on the same
He was far better

[@aqib5354]

Good afternoon sir

[@rohitchan007]

Please share the link of the notebook

[@mgaf7864]

Sir how to do feature engineer and features selection

[@soubhikbandhyopadhyay8160]

Krishna sir bohot din se apko follow kar raha hun plz pura video upload kar dijiyega ipy file ke sath plzz....

[@prashantyadav885]

waiting for this end to end project

[@muezahmed7054]

a link for the code would be very appreciated

[@sonubesra771]

1000+ likes ✅. Please provide ka whole project implementation

[@preetjainvd9909]

hi , is end to end project upload with ci/cd

[@ScholarStream_25]

@@preetjainvd9909 where can I get this code??

[@priyabharti-rz3lt]

Sir Please make video on CATBOOST Algorithm

[@datasciencegyan5145]

@krish 1000 likes completed ... pls upload end to end

[@preetjainvd9909]

Hi , is any update from Krish sir, regard end to end project and code

[@AI_DOL_]

Pls sir, is the code available?

[@nishantbansal5135]

Done

[@rangaabi8471]

Can you provide the code sir?

[@kinjalgogri2850]

it is not showing all only showing Random Forest

[@rohitsonawane4968]

Sir dsa series continue karo please

[@bradleymarimbire5603]

Hi sir can we get the code please ?

[@mohsinimam2048]

Sir please upload this project.

[@victorhenostroza1871]

x2

[@ajaykushwaha4233]

Sir one question, instead of trying each algo, can we use automated libraries like pycaret, autoML or other libraries?

[@HellRaiZOR13]

You can use Lazypredict Library it automatically tests all the available machine learning algorithms in the sklearn Library and give you the score for each of them. Based on that you can an initial decision on which algorithm to start with.
You dont even have to do the dictionary way like they have shown here.

[@bruhm0ment767]

@@HellRaiZOR13 does it allow parameter tuning?

[@HellRaiZOR13]

@@bruhm0ment767 no it will train all models on their default settings but later you can pick the best model and then do parameter tuning on those specific models.
This Library is just to make your model selection process easy and faster.

[@eskayML]

Since trees work generally better than linear algorithms(personal observation) , is there any need of using linear algorithms at all when we are finding best models

[@johnwig285]

Ofc, because not everything is about "better" performance i.e. accuracy. It depends on complexity n time n client's's expectations.

[@nashrahkarim7404]

can you provide the code ?

[@illuminati_358]

Sir plz share this end to end project

[@preetjainvd9909]

Are you got reply for end to end implementation with ci / cd, please message.

[@rohitchan007]

Please share the code.

[@mamunhsn4839]

Today Three people came to our Institute for Data Science program. But any how I convinced them not to take admission in our institute .
I highly suggest them to take admission to iNuron. You are doing a great job Sir👍🏻

[@krishnaik06]

Which Institute??

[@mamunhsn4839]

@@krishnaik06 EXCELR Raising Excellence Bangaluru

[@mamunhsn4839]

I Wish I Knew It earlier about iNuron.

[@sohanaryal]

let's make it it to 1000 likes

[@surendhrancbe7872]

Sir..I have a doubt that " in terms of complexity does the project we do in the companies is similar to the project you are doing here ?" since I pursuing degree so I don't have idea about it..that how could be the complexity of the projects in companies. Can anyone please help me out

[@tykemonster6416]

Not even close. Real world projects are some next level stuff. No work is done in Jupyter, all in visual studio mostly. Also, code is just one part of a project. A big challenge also comes in deployment where you have to dockerize it, create pipelines, then deploy it in cloud platform like Azure/AWS/GCP. Also in deployment another big challenge is some libraries n their versions are supported by some cloud platforms some are not 🤦🏻‍♂️. Then there could be some other technical challenges too in production. It really eats your brain up 😂

[@engr.FidaHussain]

too fast, hard to understand

[@illuminati_358]

1 like remaining only

[@Umesh-lz2ro]

use AutoML do nothing

[@saravanankarunamurthy9854]

Hi Krish, 1000 likes completed.. pls provide GitHub link for this code