How is data prepared for machine learning?

[@anirbansarkar6306]

This channel has got one of the best videos and visuals which gives indepth understanding. Thank you so much for such immense hardwork

[@mukundyadav6913]

The analogy goes like this: modelling, tuning the hyperparameterss and using different algorithms to get better results is like working out, whereas data is like the food you put in your body. No matter how much you workout, how good and intense and your workout sessions are, if you dont eat good, balanced, high quality food then you are not gonna be healthy and get good results. Similarly, if you dont use good and high quality, accurate data then your model will not produce efficient results(data is literally the food for the model). So, when trying to improve your model's results, always try to improve your data quality first instead of changing your parameters and algorithms!

[@abhishekmistry933]

The best explanation I've seen on KZbin

[@goldenmilktea4992]

Great content as usual. Now I have a better understanding of what machine learning is. Thank you!

[@sakshammishra9232]

Excellent use of graphics....just love it.

[@mp9305]

Excellent production quality as usual. You guys deserve more subs :)

[@dhruvgaikwad9088]

This is what I was looking for!!! Best Explanation!! Keep it up.

[@hasinistyle]

Excellent presentation on data preparation process for Machine learning.

[@attribute-4677]

This video deserves more love. Thank you!!

[@victornavorskie]

Just Wow Instantly liked the Video and subscribed to the Channel. Thanks for your hard work

[@sidlist]

Great Content in very simple language. Can you please make a detailed video on ''Feature Engineering'' - Handcrafted features and Derived features?

[@arnavsinha834]

Thanks, learning something new!

[@blip666]

it was working fine why did they discontinued it

[@amitpatil518]

Very informative video. Covers all the topics.

[@mohdriyazpm]

Very well explained

[@srimant101]

Very useful. Thanks a lot

[@Seiven2077]

AI was correct. there is nothing sexist about being statistically right. Men are statistically better achievers, they work more, prefer more technical majors and manage stress better -> get hired more for such positions because they fit better. If you want to have a balanced race and gender for your data set you will end up with average at best candidates, because some talent would be excluded as a result of Quota sampling. And this is not what you want as a HR. Gender factor is important, that is why you don't hire creepy men to work in kindergarten, nursing or daycare. Stereotypes + less success in this field statistically among men.

[@kaasboyzz]

Awesome video. Thanks!

[@somarble]

wonder why this video was suggested when I searched for "serialized data"

[@st10689]

What a video! Amazing really.

[@kennedywee]

Great Content!

[@ABDULKARIMHOMAIDI]

Thanks !!!!!

[@ahmedshalaby9343]

omg i wish if ur my professor in the college

[@BatditRwoman1]

Thank you so much

[@philipphortnagl2486]

great videos!

[@nkristianschmidt]

since the target variable was probability of job achievement, the model stuck unrecorded variables into sex. It likely needed to know more rather than be robbed of the sex variable. The model was scrapped and bias likely got worse bcs humans cannot balance information like machines.

[@RightAIopen]

D woman were actually worst...

[@marwanfudo998]

CHAD AI

[@evenodds8791]

E: men are more capable