10 ML algorithms in 45 minutes | machine learning algorithms for data science | machine learning

[@muditmathur465]

Thanks, this came really handy 1 day before interview 😁👍

[@UnfoldDataScience]

That is the purpose of this video,🙂

[@bushraaijaz2125]

Seven ML Classifiers with python using colab: kzbin.info/www/bejne/Z5SbgZxmp81gpbM

[@naveenarunkumar95]

So Easy to Understand all the concepts of ML Thank you for this

[@Srinivascheekati681]

Hi ,This Ch Srinivas ( EX Faculty in ACE academy and currently working in MADE EASY IES, I would appreciate your teaching process . Thanks for sharing your knowledge. GOD bless you. I am planning to do PhD in Data Science please give me your valuable suggestions. Thanks

[@UnfoldDataScience]

Thanks Srini, welcome to channel

[@jayalekshmik3706]

Yes I too would like to know what entails in a ML path

[@surajitchanda1975]

A very good lecture to refresh my knowledge my name is Surajit Chanda i am an instrumentation engineer and also a Software Engineer

[@swethamadhavarapu3018]

Great video, simple easy to understand explanation for beginners. Thank you!

[@user-vo9kh4zd3f]

this is very helpful video those who want to gain basic knowledge in ML algos but uh did a mistake in Gradient boost calculation in 23:44 . once check it

[@UnfoldDataScience]

Thanks a lot for watching and feedback.

[@achunaryan3418]

It's 122

[@kamaleshwaran06]

Best Video for a quick introduction/refresher on ML Algorithms. Kudos!

[@UnfoldDataScience]

Glad it was helpful!

[@tadhailu]

Very important, I need to watch it again and again.

[@dheenadhayalan423]

All the prerequisites I was hoping for was covered and explained clearly. Thank You sir !

[@UnfoldDataScience]

Thanks a lot

[@ishanagpurkar]

This is a very good video for revision of ml models.

[@UnfoldDataScience]

Thanks Isha. Please share with friends as well

[@godfreyuzonwanne3110]

This is a very simple demystification of a complex topic. Great job here. I love the very straight definition of machine learning presented here ... understanding patterns in the data using algorithms 🎉

[@explorewithskp1237]

Thank U Sir . Clearly got an idea on all algorithms in very short time ☺️

[@UnfoldDataScience]

Most welcome 😊

[@beautifulplacesinworld61]

What is the Purpose of Final Accounts in any Business?? kzbin.infoMHz-Jwgo14s?feature=share #FreeEducation

[@UnfoldDataScience]

Not relevant

[@pritus9537]

Very simple and effective method of teaching all algorithms

[@hgowda11]

This is an excellent and time-efficient video with a great explanation.

[@dvabhinav31]

Helped with understanding logistic regression!

[@Er.Sunil.Pedgaonkar]

Good -- Er. Sunil Pedgaonkar, Consulting Engineer (IT)

[@satyagarapati3611]

Great Aman!! Wonderful explanation ❤

[@itsme1674]

Machine learning is nothing but learning pattern from a data using an algorithm. An algorithm is set of steps that are executed in an order to reach final solution.

[@UnfoldDataScience]

Yes, nicely said

[@13soulmate13]

​@@UnfoldDataSciencebrother resume shortlist hi nhi hora what can i do i am fresher

[@UnfoldDataScience]

put good projects and keywords based on JD

[@geethakiran8122]

R u data scientist

[@comandozz123]

​@@13soulmate13I went w

[@leonine291]

زبردست ❤

[@romualdcieciura9902]

Suuuper. Bardzo, bardzo, bardzo dobrze wytłumaczone. Dziękuję

[@someshwarrao7357]

Very Nicely and firmly explained the concepts and usage.

[@VinayAggarwal]

This is super helpful. Thanks for putting this together. ❤ Can these all work on more then 2D data ?

[@UnfoldDataScience]

Yes Vinay. Thanks

[@atiqrehman8435]

True that

[@sagarambhore4677]

Greate teaching sir....🙏

[@mahendrachauhan63]

Nice, super Duper, you are awesome boss

[@UnfoldDataScience]

Thanks Mahendra

[@dd1278]

Thanks for this..quite a critical video for everyone who's having interview (s) lined up.

[@UnfoldDataScience]

Thanks Deb.

[@tbjapbtu10]

Thank u so much brother I am new subscriber of u r channel After seeing ur videos, i thought that i got some support in Learning of ML Ur videos are in very simple English Thank you brother

[@UnfoldDataScience]

Thanks a lot.

[@pawankumarjm]

This is the best explanation till I saw..😊

[@UnfoldDataScience]

Thanks Pawan. Please share with friends as well :)

[@vamsivegi]

Wish this kind of tutorial 5 years ago. But it’s not too late. Simply one the best.

[@UnfoldDataScience]

Thanks Vamsi.

[@tirupathimujjiga7912]

Great video meaningful and clearly explained. God bless you.

[@tadhailu]

I like the way he explains,.

[@chandrusm-c9r]

very pretty and clear explanation .stay tuned and thanks very much buddy

[@UnfoldDataScience]

Welcome.

[@anthonymary1cyril5]

thanks sir it was easy to understand

[@balajigayathribala]

Thanks, wonderful explanation.

[@damienlomath1766]

wow very educative , perfect and practical examples makes it clear, precise and concise

[@UnfoldDataScience]

Thanks so much

[@karanalang1573]

thanks for this very helpful video !

[@UnfoldDataScience]

Glad it was helpful!

[@sukumarane2302]

Good presentation . Thanks 👍

[@navya-s3v]

Artificial intelligence algorithms are vital in data science. They help computers to learn from data and generate predictions or conclusions, which are used in applications such as image recognition, natural language processing, and recommendation systems.

[@RakiatHaruna-cx8jh]

Thank you for the beautiful presentation. Could you please give an example using spatial data.

[@vasutke1187]

Sir, Ultimate Teaching Style, Sequence of arranging Topics are highly help full to us. Great

[@UnfoldDataScience]

Thanks a lot. Please share with friends also.

[@Latif127]

It looked good to me, thank you.

[@vinny-co6rl]

sir in gradient boost the average of salary (80+42) is 122 but U taken as 112 It is changing when we take 122(average is 40.666)

[@researchhub2727]

Excellent sir 🎉

[@happylearning-gp]

Excellent, Thank you very much

[@UnfoldDataScience]

Thank you

[@noormohammadatapoor3119]

Both the decision tree and Random Forest also can be used in classification tasks. Therefore they cannot be limited only to regression tasks.

[@UnfoldDataScience]

Yes absolutely. I took that in regression category to have variation of regression models.thanks for message

[@swwapnas8741]

nice way of teaching

[@Ashish_Shinde9623]

Very Informative video, thank you

[@UnfoldDataScience]

Thanks a lot.

[@NithishKumar-ng7dp]

Good Explanation Sir

[@saramohammed5421]

Thank you this is very helpful and easy to understand!

[@divyasri9529]

helpful👍

[@Paladipradeep]

It was indeed a great session, thanks

[@UnfoldDataScience]

Thank you Pradeep. Pls share with friends.

[@Paladipradeep]

@@UnfoldDataScience Already did

[@Griffindor21]

Great video! Decision Tree can also do classification as well, right?

[@UnfoldDataScience]

Yes it can. Thank you

[@ishaqmuhammadabubakar3308]

Nice one thanks

[@ajaykumargupta5862]

Great session and well explained. Thank you sir. Please create more videos to explore more.

[@UnfoldDataScience]

Thank you, I will

[@e4skitv]

Thanks . I just subscribed

[@VikasVerma-xf6hb]

Thank you. Very nicely explained. Kudos to you. Keep-up the good work.

[@UnfoldDataScience]

Thanks Vikas. Apne friends group me bhi share kar dijie.

[@spicytuna08]

wow. awesome summary,

[@UnfoldDataScience]

Glad you liked it!

[@SethiGaneswar]

Great presentation and i think this is one of the best videos on simply making understandable to the concepts. thanks for the video

[@UnfoldDataScience]

Glad it was helpful!

[@upadisetty]

great stuff, thanks

[@pramethicine9328]

Amazing video will let you know if I pass the interview 😂🙏🏼

[@UnfoldDataScience]

Cheers, good luck

[@Manoj151267]

Just scratches the surface - OK for someone who has a working knowledge and needs to brush up. A bit of a lazy presentation - at 28:00 minutes, age & salary can go from +infinity to -infinity!!

[@UnfoldDataScience]

Thanks for your feedback.

[@storiesshubham4145]

Liked it even before watching

[@UnfoldDataScience]

Thanks Shubham.

[@amoghbantwal8179]

Hi very nice video. What is the difference between adaboost and gradient boost. As far as I am understanding it, they both have a similar algorithm with residuals that decide how the next model interprets the data

[@mahmoudshanshoury5643]

Thank you for the beautiful presentation.

[@katamreddiprasad418]

Very informative. Thank u...

[@vishsara]

very useful video, thanks

[@anishvarughese9517]

Explained well

[@shivagupta2052]

Great session . Can you sir make a video regarding project where you apply all ml algorithm and also do model development and same for deep learning

[@UnfoldDataScience]

Noted

[@ganeshsubramanian6217]

Very good explanation Aman🎉

[@UnfoldDataScience]

My pleasure

[@Dropella]

this is best I have seen ever

[@UnfoldDataScience]

Thanks deelip. Pls share with friends.

[@sircosm]

Nicely explained! Very helpful.

[@UnfoldDataScience]

Thanks for watching. Keep learning

[@lakshmanthota8902]

Very handy for a quick recall

[@chiragxed]

23:40 (80+42)/3 = 122/3 = 40.6

[@satishb9975]

Thank you 🎉❤ excellent 👍

[@UnfoldDataScience]

Welcome 😊

[@kennethstephani692]

Great video!!

[@UnfoldDataScience]

Thanks for the visit

[@Jordi5720]

super useful

[@DataScienceWallah]

That's very well explained highly appreciate the content ❤❤❤

[@UnfoldDataScience]

Thanks again, please share with friends as well.

[@masoudmirzakhanloo8059]

awesome 👌

[@kishorrawat8083]

Great lecture.... 👌👍

[@UnfoldDataScience]

Thanks a lot

[@Thebeautyoftheworld1111]

please explain base model in adaBoost . It sounds similar to M1 model. is it different from M1 model. if it is so, what is the difference. Kindly explain. But great explanation.Keep up the good work sir. God bless

[@UnfoldDataScience]

Sure thank you

[@sachinmore8938]

Great informative video. Thank you for sharing your knowledge.

[@UnfoldDataScience]

Glad it was helpful!

[@ratheeshmsuresh7368]

Brother, Please help to get clarity for the Below Questions, First Question : check whether The average monthly hours of a employee having 2 years experience is 167. What will be the Null and Alternative Hypothesis that I should Consider?

[@UnfoldDataScience]

Can be framed in multiple ways

[@uditi_]

null can be “…it is 167” and alternative can be it is not, then you can prove or disprove null hypothesis

[@VISHNUPRASADSAKHAMURI]

Hi This video is very informative. thanks you so much.. Can you suggest which algorithm is best suited for below use case "scan the kuberbetes pods for application exceptions and feed the algorithm.. let the model store this info along with impact assessment, to raise the alerts only for critical exception"

[@UnfoldDataScience]

Thanks For watching.yoy can research on isolation forest or random cut forest

[@khanyijiyane1585]

Really big thank you❤

[@UnfoldDataScience]

You're welcome 😊

[@Marklodhi420]

Exceptional stuff.

[@UnfoldDataScience]

Thank you. Pls share with friends as well.

[@sureshkumar-cn5jr]

Useful content Aman! Thanks for your efforts to teach complicated but important concepts in M L

[@pankajjoshi8292]

Thank you sir , cannu pls tell how to implement these in python

[@UnfoldDataScience]

HI Pankaj, if you go to playlist section, you will find all the implementation as part of different playlists :)

[@buttterflygirl]

Need your help understanding a scenario where the OA and kappa coefficient are more or less similar on test and validation datasets when using only one independent variable. Here, the validation dataset meaning completely a new dataset in time and space. Train and Test belong to same time and space. Can you explain to me why this is? I appreciate your help on this. When run with a few more variables, this issue is not showing up. For more understanding, Train and Test are from same day satellite image for city A. Validation dataset is from different day satellite image for City B.

[@Learn_IT_with_Azizul]

Great. please keep up with e-commerce projects in ML practices. Ty

[@UnfoldDataScience]

Sure , many thanks for appreciation and suggestion.

[@raghuyadav1406]

Hi, do you have implementation examples for all these, i think decision tree, random forest available but others not, also you cover support vector, k nearest etc..

[@UnfoldDataScience]

Definitely, thanks for suggesting, will do.

[@talentzunlimited1398]

Very helpful !

[@UnfoldDataScience]

Glad it was helpful!

[@rajpandim]

Excellent explanation

[@UnfoldDataScience]

Glad it was helpful!

[@rafibasha4145]

Thanks for the video ,pls cover Naive bayes ,XGboost catboost dbscan hierarchical clustering in one hour video and all stats in 2 to 3 videos also dl nlp imp concepts in 1 hour length video s

[@UnfoldDataScience]

Noted

[@rafibasha4145]

@@UnfoldDataScience thanks

[@kanoriopurity4374]

very well detailed great content

[@UnfoldDataScience]

Much appreciated! your comments motivate me.

[@smegala3815]

Thank you sir

[@UnfoldDataScience]

Welcome

[@userhandle-u7b]

Thanks a lot for this. Very helpful! I was a bit lost at a few points such as Ada Boost & Log Regression. But that's efficient for a starter. 👍👍👍

[@UnfoldDataScience]

Thanks for watching

[@omkarbelpatre5]

Thank you so much sir

[@UnfoldDataScience]

Most welcome

[@NurserytoVarsity]

You're making education engaging and accessible for everyone. #NurserytoVarsity

[@UnfoldDataScience]

So nice of you. Please share with friends.

[@maniranga8423]

Good to start for AI