Machine Learning Algorithm- Which one to choose for your Problem?

Here is a video which helps you understand which machine learning algorithm you should use for your use case.
You can buy my book of finance with ML
#Whichalgorithmtochooseforyourproblem
amazon url : www.amazon.in/gp/product/1789...

[@nandinidasgupta7781]

This is the only channel i guess who understands what students are expecting to know other than traditional algorithm knowledge.. Thank You so much..

[@chandlerdarius6380]

sorry to be offtopic but does any of you know a way to log back into an instagram account??
I was dumb lost my login password. I would love any tricks you can offer me

[@hunainaghai3342]

Which algorithm did u choose, Nandini?

[@shubhpundir2191]

@@chandlerdarius6380 this dude 💀💀💀

[@srikanththecoolhunk]

You have no idea how useful all your videos were to help me find a job. I cannot thank you enough Sir, please post more.

[@varunsagartheegala]

Your videos is a gift to all data science aspirants like me and working professionals too. Thank you for making our learning easy and fun. Please don't stop

[@harshj84]

Hi Krish,
I really enjoyed your videos. The specialty of your videos are they fill the gap between theory and practical.
I have watched tones of other videos, most of them are telling "how to plot the graph" but no one is explaining "how to leverage information from these plots?"
Just like this video, we need to use knn as most of the points are overlapping. I would really love to such a great video for other algorithms as well. Not only me, but I believe your subscribers would also love them.
Tons of thanks to you.

[@anandacharya9919]

This is your best and most important video of all. Thank you 🙏

[@chravi511]

Hi Krish,
I've been following your channel for most of my learning.
The way you explain the things gives a very familiar approach and giving out what is needed in the way everyone can understand.
This let me watch any video of yours till the end and learn completely.
Happy to learn from you. Thanks

[@deepuraju2456]

Hi sir , can u give any idea problem statement for loan approval prediction

[@izzycrux48]

Thank you Krish for the amount of effort and insights you put into your videos. Really helps a lot🙏❤️👍 May God bless you and keep you well🙏

[@nokiyem521]

Great, this is the video i was looking for to explain the difference from a basic mathematical perspective.

[@itybahadur3625]

Excellent video for an initial understanding! Thanks!

[@vikasbohra3253]

You are AWESOME Krish not only from knowledge's point of view , but also in explaining the concepts in pretty well manner. Thanks.

[@MrDeepinaction]

Great video, friend!! Keep up the good work. Need to learn a lot through you.

[@pravinkumar54]

@Krish Naik 11:30 if you double pick the pairplot in jupyter notebook. It zooms in

[@aanalpatel5361]

Most of my questions were answered through this video!! Thank you a ton, sir!!

[@ranjaniselvaraj6475]

Good Job! Clearly explained. Thank you so much for this video.

[@ninjaduck3534]

This was so excellently explained, thank you so much!!

[@akashchoudhuri3932]

Best video till now. Thank you Krish.

[@amoghjain]

Such a practical channel with real world applications! Thankssss!!

[@sankarapandian.selvaraj]

All of your videos have a lot of useful information. Thank you...

[@ankitaB11]

Most important video. Explanation is just amazing.

[@rohinikalamkar5736]

This is what is required .. thank you so much for sharing this 👍👍 great work

[@pietart3596]

Thanks for everything you do, your words are very motivating

[@thongtech1984]

thanks a lot for this wonderful explanation, Krishna. You are my new hero now :)

[@sambhavmishra1873]

Understood it very very clearly ❤ whatever doubts that were arising was getting explained in the next second.

[@abhinav02111987]

You saved my day Krish. Thank you.

[@nagupranav]

you are always great man.. anyone can understand by watching your video.

[@mansiparikh8357]

You have explained in a great manner. Please make more such videos for data science. Very helpful

[@saunitmarolia3901]

Thank you Krish! All doubts cleared now.

[@saichoz]

Great Vid. You made things clear. Thanks

[@translatethis7765]

The thing is, you are looking at the pair-plot for only two dimensions. Although everything is overlapped when projected into pairwise space, it doesn't necessarily mean the data is not linear separatable right?

[@leoprinceznirp39]

Thank you so much. Your explanation gives good clarity. Great work. Thank you😃

[@somalkant6452]

hi krish, it was an awesome video. just a doubt popping up in my mind, when there is overlap of datapoints, why cant we use SVM, bacause that will also take the points (of different classes) to other dimensions and divide the points using hyperplane. please correct me if my understanding is not correct. Than you so much for your awesome videos.

[@KaranSingh5]

Thank you so much for this video! This solved such big confusion for me!

[@sahinur_zmn]

just awesome 💚💚💚i am just recharged after completing this video..thanks a lot.

[@khalfanalkharusi6577]

Thank you for your contribution. Please note also that classification learner app in Matlab provides you such pair plots

[@neelammishra5622]

I really found the answer of my most confusing question into so simplify terms. Thanks sir

[@amirhosseinrahimi3964]

Thanks a lot for such a great explanation 😊

[@olajideoluwasanmi4260]

You are the best Krish. Thanks for this.

[@connectsantech]

Very nicely elaborated !!! Thanks.

[@RaushanKumar-qb3de]

Thanks. I'm in need of this video

[@gargidixit5651]

Amazingly Explained, I have never got any satisfactory answer for this question Thank you so much for such knowledge sharing :)

[@gouravgangani]

Thanks Krish for such a nice explanation.

[@puneettiwari2251]

Great session,
Please make an elaborative video on this topic covering all the pros and cons of different algorithms and if possible with codes.
Thanks for this session again sir🙏👍👍

[@V_Wankhede]

Informative video , its all jam of each algorithm that you taught. Thank you for brief explanation. 👍

[@MarioLaudazi]

Thanks for the useful videos you post.

[@folashadeolaitan6222]

Thank you for this great insight!

[@kamran_desu]

Hey Krish, great video - just shows how important it is to understand the geometry/maths behind ML.
In regards to using KNN here for overlapping data points, would SVM (radial/polynomial) be a good choice as well, since it will use the kernel trick to apply a non-linear classifier in a higher dimension?
And how would SVM compare to the tree methods in terms of computation complexity?

[@adityakishan589]

Same doubt. Krish please help us get clarity on this.

[@karandua6564]

Even logistic regression may work, he is just comparing two dimensions at a time, they may be linearly separable in higher dimensions
.

[@danielschulz5447]

@@karandua6564 no, I think that is not true. LR will not work good, if single plots are pairwise highly overlapped and/or not dividible into straight lines.

[@souravbiswas6892]

awesome work Krish, a big thumbs up

[@faisalghazanfar169]

excellent explanation...really an easiest way to understand and clear confusion..:)

[@neon91111]

great video man .. luved it..

[@anupambarasia7865]

very well explained thankyou so much sir

[@chrissanjiv1753]

good explanation Krish, Very crisp and clear

[@prernamistri]

wonderful video. thank you.

[@salihsartepe2614]

This is amazing. Thank you Krish :)

[@himanshubisht9643]

Here I can understand how to choose the best algorithms to my dataset thanx sir👍👍

[@karldtrumpeter]

Thanks so much Krish. This knowledge is pricelss.

[@anujasebastian8034]

Great insight!!!
Thanks Krish!

[@gangaholi8600]

Very nice presentation.. thank you..

[@sunnysinghbhatia194]

This was very much helpful Thank you Krish

[@chandramoulireddy9636]

Naik sir, thanks for giveing inforamtion about selecting algor,,. i am very happy to follow ur channels.
thanks.

[@ainju1]

Informative. Thank you so much

[@harshinimedha7807]

The best explanation I have ever seen ✨

[@KetanChaudharyTHE-GREAT-KETAN]

Thanks a lot Krish......it's really very helpful ......

[@jigarshah1883]

We should give a try to SVN as well as using kernel we can understand the data after plotting and predict it nicely..

[@ketanbutte3497]

excellent video! thanks

[@Crazyanya_]

Sir thank you so much you solve my problem

[@suhailabessa9901]

Woow, Thank you. its helpful

[@AbhishekBalsara]

Great video, really cleared the concept 👏👏

[@Tjos4155]

classic video... cleared my concept

[@swetapatra]

ok, so ideally, we decide on the algorithm based on the charts we have plotted?

[@barsvelioglu2276]

Amazing! Thank you.

[@yunus9850390381]

Hi Krish,
First of all, it was a really good video and nice explanation. Thank you for sharing.
I would like to understand, what will be the visualization option when we will have mixed data ( Numeric and Categorical)?

[@syedtasleem2827]

Sir, What about SVM U didn't mention on what kind of Data it can be used after understanding pairplot,..

[@Raja-tt4ll]

Very useful video. Thanks :)

[@devmaharaj4640]

One of best video's I have seen , won't forget to return u the favour....

[@sithalrao]

Hi Krish, if we have more numbers of features say 30, than in that case we wont be able to draw pair plot. What should be our approach in that scenario? Many thanks for your wonderful videos :)

[@mithunkumar7063]

what if there are more number of categories..the graph takes time and will it be useful then???

[@modernmonk17]

I tried to plot the same on my datset & it shows a mix of overlapping in some features & non-overlapping in others,so based on that which model should i go for?
Any help would be appreciated

[@siddhantkaushal7669]

It is a good video. Also explained in detail to understand pair plots. :) Thanks..
My Question is:
When should we decide to use SVM and Naive by visualizing and analyzing the pair plots? :)

[@datahat642]

@siddhant Naive Bayes works well with higher dimensionality (such as text corpuses) while support vectors whpith its kernel is applied when the data is non linear but you want to apply a linear classifier by transformation

[@yogitapeshane6934]

It was really good video.. Please make more videos in DataScience...

[@rupeshghule8495]

Thanks alot pretty informative

[@shreyasb.s3819]

Very nice explanation

[@harshitatiwari4917]

Sir , this vedio is very helpful

[@Amansingh-tr1cf]

U made my day Krish sir

[@kunikakhobragade6953]

great sir ...
can u plz make a video on the comparison of every ML algorithm

[@paramjeetgill1558]

Excellent👍

[@mumtahinaparvin7668]

Well explained.

[@nageshpindi1348]

Does it works for regression techniques, I'm trying but not able to generate plots as you showed.

[@WasimAkram-vq5if]

Thanks alot

[@keerthanabalaji3584]

Thank you very much. :)

[@Rajkumar-vc2pg]

You are my true datscience Guru 🙏🙏

[@srinivasrathodkethavath9545]

Thanks...

[@drm8164]

Love you, you are great

[@vamsikrishna8186]

Thank you…

[@vineethp8925]

Superb Explanation can you please do on regression

[@srikantabiswas2613]

great seassion sir ]

[@bakkiyarajganesan3130]

I got an idea for Machine Learning algorithm selection. l blindly go with XG boost for nonlinear and imbalance dataset, will get decent results. Your videos are helpful for us and keep doing it 🙏

[@manikantabandla3923]

Does all the classification problems solved using neural networks?

[@illuminati_358]

Very useful video krish sir🥰🥰

[@tanushreenagar3116]

Nice thnk you

[@chravi511]

Please Explain me,
Scenario 1 : All Categorical, ordinal, nominal features - Categorical target
Scenario 2 : All continuous features - Categorical target
Scenario 3 : Combination of categorical and Continuous features - Categorical target
Which model to use in these scenarios (particularly SCENARIO 1) ?

[@rajivturkar1671]

Great explaination Krishna.. I would like to know how pairplot will show categorical variable and how we will do the feature engineering for categorical variable?

[@sejalchandra2114]

Yes, this is my doubt also. Please help!!

[@manikaransingh3234]

@@sejalchandra2114 You first have to do the label encoding to your categorical features. and then use the pairplots.