Feature Selection Techniques Explained with Examples in Hindi ll Machine Learning Course

Data Science Noob to Pro Max Batch 3 & Data Analytics Noob to Pro Max Batch 1
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Myself Shridhar Mankar an Engineer l TH-camr l Educational Blogger l Educator l Podcaster.
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[@heenarupabheda6021]

That line "Aaj ka video bahut hi kamal ka hone wala hai"..😄
Sir aapka har video kamal ka hota hai..😀

[@tanvisshah26]

Watched you when I did my Bachelor's, watching you now when I'm doing my Master's!

[@shreyxsh5054]

@NITESH KUMAR zila parisad

[@bhartichambyal6554]

You still don't know

[@RajSharma-nr9hw]

same here

[@amitbparmar3534]

Where u doing masters?

[@RajSharma-nr9hw]

@@amitbparmar3534 at Gujarat technical university

[@humancodex]

Vai Real engineer ho, salute.

[@41abhishek]

Excellent tutorial.
But, regarding embedded method.. (as per my understanding) the algorithm itself filter the unimportant feature. The best example is regularization.
Ridge and Lasso regularization in liner regression remove or vanish the unimportant feature coefficient (As their coefficient is already low and after applying regularization it will become zero).

[@MrAyandebnath]

Very nice explanation..short and compact..i love the way u make us understand...I am so happy after watching your video that
I subscribed your channel to learn more from you

[@nidhi10sharma]

Such interesting videos on topic which i was finding difficult to understand and boring earlier. Now, able to understand it in just a span of 5-10 minutes in the most easy and interesting manner.
Thank you so much!!!

[@akshayjadhav3004]

Got my B.E. Result Today with Distinction.. Thank you so much sirjii for such smooth Teaching..😍

[@bhavikdudhrejiya852]

This are the comprehensive list of various feature selection
1. Filter Methods
A. Basic Filter Method
1. Constant Features
2. Quasi Constant Features
3. Duplicate Features
B. Correlation Filter Methods
1. Pearson Correlation Coefficient
2. Spearman's Rank Corr Coef
3. Kendall's Rank Corr Coef
C. Statistical & Ranking Filter Methods
1. Mutual Information
2. Chi Square Score
3. ANOVA Univariate
4. Univariate ROC-AUC / RMSE
------------------------------------------------------------------------
2. Wrapper Methods
A. Search Methods
1. Forward Feature Selection
2. Backward Feature Elimination
3. Exhaustive Feature Selection
B. Sequential Floating
1. Step Floating Forward Selection
2. Step Floating Backward Selection
C. Other Search
1. Bidirectional Search
------------------------------------------------------------------------
3. Embedded Methods
A. Regularization
1. LASSO
2. Ridge
3. Elastic Nets
B. Tree Based Importance
1. Feature Importance
------------------------------------------------------------------------
4. Hybrid Method
A. Filter & Wrapper Methods
B. Embedded & Wrapper Methods
1. Recursive Feature Elimination
2. Recursive Feature Addition
------------------------------------------------------------------------
5. Advanced Methods
A. Dimensionality Reduction
1. PCA
2. LDA
B. Heuristic Search Algorithms
1. Genetic Algorithm
C. Feature Importance
1. Permutation Importance
D. Deep Learning
1. Autoencoders
------------------------------------------------------------------------

[@ravishankar2180]

main topic : Dimesionality Analysis
type : 1. feature selection 2. feature extraction
1 - 4 : feature selection (here we just eliminate the features based on analysis)
5 : feature extraction (here we combine two or more features )

[@manasmore8893]

prime example of over-fitting

[@mohammadsaif3677]

Your explanation delivery is too good... people connect with u ... Good stuff mate.

[@sam9620]

The best channel i have found so far for my data mining course. 100/100

[@sadaqathussain1310]

Waah.. Kamal Krdia Sir g, behtreen. Is se se asan koi tariqa shayd koi nhe hoga beginners ko smjhany ka. Thankyou

[@mehulsoni2300]

jabardast bhai...thanks to teach in interacive way....kamaaal ka enthusiasm he apka

[@alirauf9022]

Aik dam baraber bhaiyya, Aik dam baraber.

[@Theiteducation2015]

Excellent.. one.. this is first video ... i saw.. and it 100% give me understandings...

[@ninjawarrior_1602]

Please let me know can we use any of these techniques in an unsupervised learning Clustering problem where there is no target variable

[@rohitthareja1019]

Awesome explanation!

[@randomperson7009]

Only 4 words: You are the BEST.

[@rahulchadar9096]

Sir ji itne dino se kaha the aap ab to Engineering bhi khatam hone wali h ,,, pahle hi mil jate 👍👍

[@aishwarya8078]

What an explanation... Hats off

[@akashprabhakar6353]

Thank you sir....your way of teaching is very lucid ....

[@sandykumar5350]

Awsome ..Thank you!!!

[@dineshjoshi4100]

Hello, Thanks for the explanation. I have one question. My question is, Does using best features helps to reduce the training data sets. Say I do not have a large datasets, but I can make independent variable that is highly corelated with the dependent variable, will it help me reduce my traning data sets. Your response will be highly valuable.

[@AMANVERMA-bq8hj]

Nicely explained.Thanks a lot sir !

[@Rana2058]

awesome Dear....

[@zahidjaan1319]

Thank U Engr. Bhai !

[@aniketkumar967]

sir you are an amazing teacher. Hats off you sir🧡

[@bhanuprataprai6531]

ultimate bhai.very nice explanation, n method to teach

[@santoshsahu-oy5vo]

Really amazing dear...
Thanks a lot for your dedication...
Really it is appreciable!!!

[@umangsaluja8034]

Well explained!! Please make some videos for hands on practice using tools.

[@ppuppu1172]

Your explanation is very easy to understand...

[@muktadhopeshwarkar5687]

Very nice explanation.. In a very easy manner..

[@ujjalroy1442]

Fabulous explainers....

[@versatilenick2209]

dude you have make it so interesting hats off

[@devotion_surya3741]

Watching this video before exam , its very much helpful

[@Zeeshan18382]

very very nice information for us thx allot brother

[@praveenpanikar6415]

Sir Thanks a lot for your help.. i have watched, shared and liked every video.. :)
please upload more videos of Machine Learning...

[@mdriadulhasan5777]

Excellent tutorial

[@sonu-mb3nh]

Best explanation sir... Great 🎉❤

[@HarshChaturvedi26121996]

Great explanation!!

[@ansarbashashaik4345]

Thank you sir thanks a lot you helped lot of people like me thank you very much

[@yashbastawad2331]

Superb excellent 👍

[@poojabijlani78]

Sir your explanation giving deep learning of ML Thankuuuuuuu

[@hemantkumardas3333]

Very Nicely Explaining Sir...

[@ashita1130]

Hello,
Could you please explain RFE in depth with some coded example?
Thanks great videos.

[@george4746]

Your videos are fabulous short and to the point. Can you tell me the book which you're following?

[@yeshuip]

bhai you deserve more subscribers

[@gulfrazahmed684]

very good way for understanding a topic

[@hiteshverma6722]

Could you please make videos on coding too using all the technique i,e EDA, model buliding and all the steps

[@hassantufik5750]

vai, so good you are.......

[@vaghii6731]

Superb teaching

[@AIRSTRIKE_]

aree sir ji thanks i will comment after todays paper >>>>>>>>>

[@priyankab5527]

Please upload video on Data scaling and Normalization.

[@learndaily964]

Thanks a lot sir❤❤

[@aartitatiya8275]

Sir plz upload the video of 2-3 unit of machine Learning.... exam he sir plzzz

[@ssdheeraj6347]

Wow good explanation

[@shrutishelke7752]

Sir,Plz upload ur videos on OPEN ELECTIVE subject BUSINESS INTELLIGENCE

[@maxpayne880]

Sir just once do AES and DES encryption

[@bhavikdudhrejiya4478]

Nice way to explained.
Learning points:
1. What is feature selection?
2. Why We require feature selection?
3. Why this model has low efficiency?
4. Optimal selection of the feature
5. Techniques of Feature Selection
a. Filter Methods: 1.IG 2. Chi-Square Test 3. Correlation Coefficient
b. Wrapper Methods: 1. Recursive Feature Elimination 2. Genetic Algorithm
c. Embedded Methods: Decision Trees
6. General Version of Filter Methods
7. General Version of Wrapper Methods and Embedded Method
8. What is wrapping?
9. Generate multiple models with a different subset of features
10.Difference Between Wrapper Methods and Embedded methods
11. Advantage and Disadvantages

[@ASh-hb1ub]

Very informative lecture.thank you very much sir👏👏👏💐💐👌👌👌👌👌👌👌👌👌👌🌹🌹🌹🌹🌹🌹🌹🌹 🌹

[@veerabehal151]

sir chi square nd Ig jo yha hei aap bol re teach kiya plz link share krdo

[@aamirhussain8447]

Can you upload one video on factor analysis and dummy reduction??

[@puspitachatterjee4595]

Bhaiya, aap GridSearchCV..... confusion matrix ke upar kuch video banake dijiye please... I am your subscriber.

[@user-rx5kq6oo9y]

Sir but i do bigmart sales prediction model
In this model if i take only revelant attribute and if i take all the attribute then i found more number number of attribute gives more accurate prediction .please help me out

[@lakshayjeetswami489]

sir your videos r really good... i get the best results to the topics here.. but I want to request more videos... there r a lot more topics in ML which u haven't completed... so just help me there... i am from RTU kota. my university have some unexpected works on this course... i mean the topics r not sequential and all.. in some bad way only.. help me please...

[@rajeshahir_gj6765]

How do I apply feature selection methods in unsupervised learning?

[@codingtrack4237]

Watching from Pakistan

[@anaswahid8520]

Sir variable selection methods multiple regression me Jo h us par video banwaye
I.e forward, backward and stepwise selection method in multiple linear regression Jo h

[@angadkadam2844]

Sir Nyce explaination...But recersive feature,does that take reverse also...for eg SAY ABC THEN AB,AC,AD...BUT WILL IT TAKE REV ALSO LIKE IF AB THEN BA ALSO,IF AC THEN CA ALSO,IF DA THEN DA ALSO AND SO ON..OR TAKE 3 LIKE ABC THEN ACB,BAC,BCA,CAB,CBA AND SON ON DEPENDING ON THE ROW LENGTH...PLS ANSWER ASAP

[@muhammadasim551]

Good job

[@TravelDiaries254]

Please upload the video of isotonic regression

[@susantkumarpal6418]

Sir, kindly produce a video on hypothesis space and inductive bias .

[@abhishekjain1438]

good explanation

[@jeelpanchal6653]

Make a video on Feature Extraction Method with Examples

[@abhijeetranmale8704]

bhaiji please 10th march tak machine learning cover krlo...sirf numericals bhi chalenge

[@shakifrauf2204]

Sir Fantastic....Sir aap please python bhi lo sir...

[@harib879]

Bhaiya me ek hi like kr sakta hu baki apke sab video k 100 likes bante h, obviously me mere groups me share kruga

[@18prasandkumar35]

❤❤❤ Thanks anna

[@sonamkori8169]

Thank you

[@tanveergulzar4715]

I think out of 3.80 lakh subscribers,3.70 lakh subscribers are the ones who study one day before the exam😂😂😂.You are a genius.Thank you .

[@ankeshbobade8240]

well explained sir

[@mfaraday4044]

Thank you sir

[@shwetakaul3758]

But decision tree is a classic example of overfitting model. So how can you say that embedded is better wrapper method in terms of overfitting?

[@gayatridevadiga1633]

Super..

[@RishikeshGangaDarshan]

PCA is use for dimension reduction so why we use other techniques for future selection. Please clear my doubt

[@sn_creation1999]

Awsm❤️

[@safeerkhan8706]

at 2:25 ap ne kaha k paraya tha wo video kaha hai pls share a link

[@kundankaushik3006]

sir what is Hybrid filter-wrapper feature selection .... please espe v ek bana do video

[@b_solutions6388]

Can you please help or guide me I was feeling very difficult to generate dataset and model to chose

[@soniyasharma849]

Hi sir, hope going well! Can you please make toturial on Django For interview base?

[@sandeeprathod283]

how will you select target feature in unsupervised machine learning

[@TheArchit1]

Nice

[@RMs414]

Dear need video about Feature selection methods using pyspark. kindly make it.

[@shakifrauf2204]

Sir Aap excellent ho. Sir aap python pay machine learning sikhaye please please

[@shashankraj6662]

Is feature selection problem correlated with regression...?

[@richakushwaha1759]

Sir make vedios on nlp plz we are in need of it

[@college_lyf22]

In which case we shud use filter methods...

[@sahilgaikwad8825]

Sir please make video of Scikit Learn Datasets

[@hometvfirestick]

Thanks

[@sujoychatterjee6950]

Sir in practical i have 80000 fratures for each image. Say Such 500 images are there. So the dimension is 80000*500. Now i have to apply pca for dimensionality reduction. But nowhere i find the technique. Please help me.

[@SandeepSharma-md2eb]

please arrange playlist video in some sequene..