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

@shreyxsh5054

3 жыл бұрын

@NITESH KUMAR zila parisad

@bhartichambyal6554

3 жыл бұрын

You still don't know

@RajSharma-nr9hw

2 жыл бұрын

same here

@amitbparmar3534

2 жыл бұрын

Where u doing masters?

@RajSharma-nr9hw

2 жыл бұрын

@@amitbparmar3534 at Gujarat technical university

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

Vai Real engineer ho, salute.

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

3 жыл бұрын

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

7 ай бұрын

prime example of over-fitting

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).

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

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!!!

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

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

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

Aik dam baraber bhaiyya, Aik dam baraber.

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

Only 4 words: You are the BEST.

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

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

Awesome explanation!

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

Watching this video before exam , its very much helpful

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

Sir your explanation giving deep learning of ML Thankuuuuuuu

What an explanation... Hats off

Awsome ..Thank you!!!

dude you have make it so interesting hats off

awesome Dear....

Your explanation is very easy to understand...

Excellent tutorial

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

Thank U Engr. Bhai !

Fabulous explainers....

Nicely explained.Thanks a lot sir !

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

Thanks a lot sir❤❤

very good way for understanding a topic

Great explanation!!

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

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

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

Superb teaching

Superb excellent 👍

Best explanation sir... Great 🎉❤

ultimate bhai.very nice explanation, n method to teach

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

Sir just once do AES and DES encryption

Please upload video on Data scaling and Normalization.

Very Nicely Explaining Sir...

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

very very nice information for us thx allot brother

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

Wow good explanation

bhai you deserve more subscribers

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

Watching from Pakistan

Thank you sir

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

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

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

Thank you

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

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.

Please upload the video of isotonic regression

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

good explanation

Good job

well explained sir

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

Make a video on Feature Extraction Method with Examples

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

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

Nice

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

❤❤❤ Thanks anna

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

Awsm❤️

Super..

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

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

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

Thanks

Sir please make video of Scikit Learn Datasets

Thanks........

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

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 .

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...

wah !!

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

please add a tutorial on VC dimensions

please arrange playlist video in some sequene..

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

Please sir PCA Ka video banaea.

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

Nice video. If you want to get more details then you can visit CSForum for image processing.

Super

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

Sir ur teaching style is very good ...but can u please teach the content in English so a normal people can also understand 😊

Ple explain this topic : Matlab method Neural network toolbox and fuzzy logic toolbox Unsupervised learning neural network Simple implementation. Of artificial neural network and fuzzy logic

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

plz upload "PARTIAL LEAST SQ [PLS] METHOD- Explained with a numerical example

00:01 Feature selection techniques are crucial for attribute selection. 01:35 Feature selection techniques are essential for optimizing machine learning models. 03:15 Feature dependency and correlation 04:52 Correlation between attributes and the target variable is important for feature selection 06:27 Feature selection techniques include recursive feature elimination and genetic algorithm. 08:03 Feature selection helps in generating multiple models with different feature subsets. 09:48 Feature selection is important for machine learning model building. 11:29 Feature selection techniques help in reducing computational expenses and avoiding overfitting. Crafted by Merlin AI.

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

How do I apply feature selection methods in unsupervised learning?