What is the difference between Parametric & Non-Parametric ML Algorithms?

In this video, we talk about the difference parametric and non-parametric machine learning algorithms. We also discuss how to tackle the interview questions around this correctly and effectively.
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Share your thoughts, experiences, or questions in the comments below. I love hearing from you!
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✨ Hashtags✨
#machinelearning #deeplearning #datascience #interviewquestion
⌚Time Stamps⌚
00:00 - Intro
03:37 - Defining a parametric ML algorithm
11:00 - Classifying algorithms into parametric and non parametric

[@achendvankar]

Excellent video. Wonderfully explained. What I gathered from it is as follows: 1. Whenever you make an assumption of the function of your data, then it is a parametric ML algorithm (Linear Regression). However, if you do not make any assumptions about your function, then it is a non-parametric ML algorithm. So if you try to find out y=f(x), then it is parametric ML algorithm. 2. If the number of parameters does not grow with respect to the number of rows present in your data, then it is a parametric algorithm. However, if the number of parameters grow with respect to the number of rows present in your data, then it is a non-parametric algorithm. 3. It is incorrect to assume that non-parametric algorithms do not have parameters. It is just that they change or rather grow with the number of rows in your data.

[@tamil_moviezzz]

Wonderful explanation... keep doing what you are doing.

[@ashwinidikonda2146]

Your explanation is amazing sir Please continue this playlist

[@77sayak]

Thanks for such a simplistic explanation ❤

[@technicaljp8151]

Thank you sir provided by some knowledge

[@saptarshisanyal6738]

Great great explanation

[@ShubhamSharma-gs9pt]

thanks for the great explanation!!

[@shubhamtelang9127]

Thank you sir for this playlist

[@MonikaSingh-nu5sg]

best explanation 👍

[@vedprakashyadav1334]

great explaination sir

[@nikitasinha8181]

Bestest explanation

[@arvindhh3720]

Super Explaination

[@ammujacob6508]

Amazing explanation. Thank you so much.... Linear regression is parametric then what about multiple linear regression?

[@balrajprajesh6473]

Best!

[@Neerajsingh-th3kc]

sir, when you are going to launch other question answers

[@Sara-fp1zw]

thankyou sir

[@geekyprogrammer4831]

Please continue tutorials in English. You explained it flawlessly!!

[@kotesh634]

Nice

[@marcusmitchelle1488]

what are parameters for decision trees? just like slope and interceptor for linear regression.

[@lol-ki5pd]

Parameter are the The Nodes or split point of each feature. take example of cgpa and job_selection: If node is cgpa and it says if cgpa is greator than 5 , you will get job else you will not so on point 5 , consider a line below which all failed and obove which all passed. This is line, again when we go to higher dimension, line becomes plane and more

[@binarystar4947]

[@YadavSachin01]

Hindi