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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Data Science for Business By Foster Provost - amzn.to/3ajN8QZ
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Practical Statistics for Data Scientist By Peter Bruce - amzn.to/37wL9Y5
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Introduction to machine learning by Andreas C Muller - amzn.to/3oZ3X7T
The Hundred Page Machine Learning Book by Andriy Burkov - amzn.to/3pdqCxJ
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[@mrutyunjayaraghuwansi4368]

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

[@UnfoldDataScience]

Thanks Mrutyunjaya.

[@shanmukhchandrayama8508]

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

[@UnfoldDataScience]

Thanks a lot Shanmukh 😊

[@kalyanbikramadhikari6049]

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

[@HimanshuKumar-oi8qh]

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

[@brahmadanna]

Every one can understand ur explanation.....neat and clear 👍

[@UnfoldDataScience]

Thanks a lot Brahmadanna.

[@PramodKumar-su8xv]

good explanation with keeping the audience understanding in me

[@sudhanshusoni1524]

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

[@UnfoldDataScience]

Welcome Sudhanshu.

[@ajitkulkarni1702]

Amazing explaination Sir !!!

[@spivak4342]

THANK YOU MR. AMAN SIR

[@yogeshbhatt3806]

Awsome description

[@miteshkumarsingh]

Very Well explained.

[@dehumanizer668]

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

[@UnfoldDataScience]

Welcome :)

[@sanyamsingh4907]

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 UA-cam 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

[@mustafizurrahman5699]

Awesome sir

[@GauravExplorations]

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

[@UnfoldDataScience]

Thanks Gaurav.

[@inderjeetsingh5959]

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

[@UnfoldDataScience]

Thanks Inderjeet.

[@kirandeepmarala5541]

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

[@UnfoldDataScience]

Thanks a lot Kirandeep.

[@sujeshlx]

Great...

[@sandipansarkar9211]

fnished watching

[@navoditmehta8833]

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

[@UnfoldDataScience]

Yes Navodit, next video me wahi aaega.

[@Engineer_Boy_01]

Thanks vaiya😊

[@user-gp4xu1sl3z]

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.

[@iqbalbawa8875]

Thank you for the explanation. Would have been useful to see how this would work in practice using an example in Excel using a small dataset or in Stata.

[@UnfoldDataScience]

Glad it was helpful!

[@shine_through_darkness]

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

[@ArunKumar-pu9ko]

Which is the best/better regularization technique , and which is used for variable selection

[@ilikasharma7957]

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

[@adwaitkotewar4682]

Sweet Spot ❌ Technical word - Balanced Fit

[@kshitizkhandelwal9348]

Sir since we already have learning rate to arrive at the optimum coefficients then why do we need to use Regularization ? Aren't both of them serving the same purpose?

[@nishah4058]

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?

[@sandipansarkar9211]

finished watching

[@keemsterbaas7156]

Excellent explanation. Subscribed!

[@UnfoldDataScience]

Thanks Keemster

[@georgechristy11]

i wish this channel reached 100K very soon

[@UnfoldDataScience]

Thank you so much. If you guys keep liking and sharing, anything is possible. Your feedback is highly appreciated!

[@christysujatha]

Very clear explanation 👍👍👍

[@UnfoldDataScience]

Thanks Christy.

[@adarsh5856]

Amazing Teaching Sir.. Thank You....

[@UnfoldDataScience]

Welcome Adarsh

[@souravbiswas6892]

Excellent explanation 👍🏻

[@UnfoldDataScience]

Thanks Sourav.

[@subhajitdutta1443]

Great... Helped a lot..

[@UnfoldDataScience]

Thanks Shubhajit.

[@homosapien7754]

Wouldn't cubing the slope(instead of squaring) in the ridge regression penalty decrease the loss function even more? If yes, why don't we do that?

[@SoumyaDasgupta]

Squaring a function makes it differentiable. Hence.

[@sane7263]

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

[@yogeshbabu3106]

bro how do you find the equation (bo+b1) after find the fist cost function is high

[@abhishekkhairnar8318]

nice explanation

[@UnfoldDataScience]

Thanks and welcome

[@TheOraware]

what do you mean by L1 and L2 regularization works only with Linear regression, decision tree based algo does have other way of regularization? You mean to say L1 and L2 are not in tree based algo? L1 and L2 are also used in decision tree based algo for example catboost regression has L2 (l2_leaf_reg) regularization technique

[@vishalreddyb9683]

Sir, can you please make computer vision and CNN videos?

[@beautyisinmind2163]

sir can we use Lasso and ridge for feature selection in multi-class classification? say for IRIS data? or it is only for binary problem? please reply

[@UnfoldDataScience]

Mulyi class also you can

[@prasannakumarthambharapu1449]

sir, can you make more videos on deep learningg

[@shaelanderchauhan1963]

I am not sure but we use l2 in neural networks i saw Andrew Ng lecture.

[@0SIGMA]

Bro please include a exercise that uses all these.

[@UnfoldDataScience]

Thanks Aaron for watching. Will do.

[@jolittevillaruz5234]

Is it possible to use ridge regression to impute univariate time series? Thanks

[@UnfoldDataScience]

we can do.

[@ravikumarranga8633]

Linear Regression => (XTX)-1XTY
Ridge Regression: (XTX+PI)-1XTY where P is penalty, I is Identity Matrix
Lasso Regression=>please mention
Elastic Net =>Please Mention

[@pinkakaur156]

If my slope is coming very less but I want my model slope to be more what's thought in that

[@UnfoldDataScience]

Slope will come based on data, why u want to change it?

[@eduardocasanova7301]

Hi Aman, BIG FAN OF YOUR WORK!! I noticed you give DS training and filled the google form right away! Sadly didn't receive any email. Can you help me with my issue? Should i receive an email? I'm super interested.
Thank you!

[@UnfoldDataScience]

Hi ,not getting enough bandwidth for training now, however I am working on a course, will share the update soon, many thanks for watching videos and staying connecetd.

[@eduardocasanova7301]

@@UnfoldDataScience Thanks for the quick response! Already turned on the notification bell!

[@sagarvarandekar8279]

My lasso regression is getting wrong results. It is giving all coefficients as zero except the constant and R2 score as --0.001825328970232576. Someone please help.

[@Krishu_Patil2411]

Exact what i want AMAN

[@UnfoldDataScience]

Thanks Vishal.

[@mukeshkund4465]

Why L1 regularization creates sparsity ???

[@UnfoldDataScience]

Hi Mukesh, does it?

[@mukeshkund4465]

@@UnfoldDataScience yes Aman it creates sparsity

[@ayushtailor9941]

samjh nhi aaya bhai.where to use l1 and where to use l2? try explaining in dnn model

[@rutviksavaliya2642]

not understood

[@UnfoldDataScience]

Feedback taken, thanks 🙂

[@Relaxing143music]

Bhai Hindustan me rhete ho toh hindi me bhi samjho na

[@UnfoldDataScience]

It's not about staying in India or anywhere else.
Unfortunately we need to speak in English in office and interviews and this channel is completely in English so that everyone can understand.