Regularization in machine learning | L1 and L2 Regularization | Lasso and Ridge Regression

Regularization in machine learning | L1 and L2 Regularization | Lasso and Ridge Regression
Hello ,
My name is Aman and I am a Data Scientist.

About this video:
In this video, I explain about Regularization in machine learning. I explain why Regularization is needed in machine learning and what are different ways to Regularize models in machine learning. I also explain about lasso and Ridge regression and explain the mathematical intuition behind it.
Below topics are discussed in this video.
1. What is Regularization in machine learning
2. Bias Variance trade off
3. What is L1 and L2 Regularization
4. What is Lasso and Ridge Regression
5. What is use of model regularization

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[mrutyunjayaraghuwansi]

After my data science classes I used to watch the concepts through your videos and it helped me a lot in understanding... 😃😃

[brahmadanna]

Every one can understand ur and clear 👍

[shanmukhchandrayama]

Beautiful explanation sir, I regret of not watching this video before my interview but anyhow I am glad I got to know it now.

[kalyanbikramadhikari]

loved the way u teach and your voice is amazaing. I wish for the growth of this channel

[HimanshuKumar-oiqh]

Thanks ! all doubts cleared ..!
The word sweet spot can actually impress the interviewer I guess :)

[sudhanshusoni]

Amazing, your teaching skills are really awesome sir! Thanks for this great work

[dehumanizer]

Excellent teacher. Thank you sir for such a wonderful explanation. :)

[navoditmehta]

Very nicely explained 💯💯
Please explain the maths behind feature selection using lasso and not ridge.

[kirandeepmarala]

Really Learned a lot Sir..your teaching skills are amazing..Super..

[shine_through_darkness]

feels like getting a lecture from one of my friends at last night before the exam

[GauravExplorations]

one of the good explanations i have seen for this topic, good work

[PramodKumar-suxv]

good explanation with keeping the audience understanding in me

[ArihantJain-hx]

Hi sir, thanks a lot for such valuable videos and crisp information.

Can you please tell me why exactly a high coefficient value is a problem in regression models? Also is very low coefficient values also a problem?

Thanks in advance.

[georgechristy]

i wish this channel reached 100K very soon

[sane]

1:09 that laugh 🤣🤣
I understand the struggle.

[nishah]

U told that l1 and l2 is only available for regression. But I have seen them for feature selection for textual dataset(although in textual data features are transformed into vector form and have numerical values) . So pls clarify the things that whether they used for feature selection also?

[ilikasharma]

Thank you for informative video, how is accuracy less is in overfitting scenario?

[inderjeetsingh]

Thank you sir .
You just made tuff topics so easy.🙏

[sanyamsingh]

Hi aman can I request you to make a video on what's the best approach of dealing with complex data in real world . as we know in real time the data is very unstructured and most of the time data doesn't exist in CSV form. But unfortunately many of the learning available on YouTube is in perspective of analyzing data which is in CSV form . Can you please enlighten these points in your upcoming videos including the best and practical approach . For example how to work with JSON data in data science project, , how to work with XML files etc?
Regards
Sanyam

[adwaitkotewar]

Sweet Spot ❌ Technical word - Balanced Fit