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
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.
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
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.
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 🎉
Very good Video. As a beginner i understood the basics well. Definitely will recommend to my students. Thankyou for the effort you put into the Presentation.
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"
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
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?
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
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
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.
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..
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
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!!
So Easy to Understand all the concepts of ML Thank you for this
Thanks, this came really handy 1 day before interview 😁👍
That is the purpose of this video,🙂
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
Thanks Srini, welcome to channel
Yes I too would like to know what entails in a ML path
Great video, simple easy to understand explanation for beginners. Thank you!
A very good lecture to refresh my knowledge my name is Surajit Chanda i am an instrumentation engineer and also a Software Engineer
Very important, I need to watch it again and again.
All the prerequisites I was hoping for was covered and explained clearly. Thank You sir !
Thanks a lot
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.
Yes, nicely said
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put good projects and keywords based on JD
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Best Video for a quick introduction/refresher on ML Algorithms. Kudos!
Glad it was helpful!
Very simple and effective method of teaching all algorithms
This is a very good video for revision of ml models.
Thanks Isha. Please share with friends as well
This is an excellent and time-efficient video with a great explanation.
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
Thanks a lot for watching and feedback.
It's 122
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.
Great Aman!!
Wonderful explanation ❤
Very Nicely and firmly explained the concepts and usage.
Helped with understanding logistic regression!
Good -- Er. Sunil Pedgaonkar, Consulting Engineer (IT)
Suuuper. Bardzo, bardzo, bardzo dobrze wytłumaczone. Dziękuję
Greate teaching sir....🙏
Both the decision tree and Random Forest also can be used in classification tasks. Therefore they cannot be limited only to regression tasks.
Yes absolutely. I took that in regression category to have variation of regression models.thanks for message
Thank U Sir . Clearly got an idea on all algorithms in very short time ☺️
Most welcome 😊
Great video meaningful and clearly explained. God bless you.
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 🎉
Thanks for this..quite a critical video for everyone who's having interview (s) lined up.
Thanks Deb.
Thanks, wonderful explanation.
Good presentation . Thanks 👍
Thank you for the beautiful presentation. Could you please give an example using spatial data.
very pretty and clear explanation .stay tuned and thanks very much buddy
Welcome.
Wish this kind of tutorial 5 years ago. But it’s not too late. Simply one the best.
Thanks Vamsi.
wow very educative , perfect and practical examples makes it clear, precise and concise
Thanks so much
This is the best explanation till I saw..😊
Thanks Pawan. Please share with friends as well :)
This is super helpful. Thanks for putting this together. ❤
Can these all work on more then 2D data ?
Yes Vinay. Thanks
True that
I like the way he explains,.
Nice, super Duper, you are awesome boss
Thanks Mahendra
thanks for this very helpful video !
Glad it was helpful!
thanks sir it was easy to understand
Thank you this is very helpful and easy to understand!
Thank you for the beautiful presentation.
Great video!
Decision Tree can also do classification as well, right?
Yes it can. Thank you
It looked good to me, thank you.
زبردست ❤
It was indeed a great session, thanks
Thank you Pradeep. Pls share with friends.
@@UnfoldDataScience Already did
Very good Video. As a beginner i understood the basics well. Definitely will recommend to my students. Thankyou for the effort you put into the Presentation.
So nice of you. Please share with friends as well. Welcome to Unfold data science family :)
Very Informative video, thank you
Thanks a lot.
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"
Thanks For watching.yoy can research on isolation forest or random cut forest
Very good explanation Aman🎉
My pleasure
Good Explanation Sir
Useful content Aman!
Thanks for your efforts to teach complicated but important concepts in M L
Excellent sir 🎉
Nicely explained! Very helpful.
Thanks for watching. Keep learning
Very informative. Thank u...
nice way of teaching
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
Noted
Thank you. Very nicely explained. Kudos to you. Keep-up the good work.
Thanks Vikas. Apne friends group me bhi share kar dijie.
Very handy for a quick recall
very useful video, thanks
Great session and well explained. Thank you sir. Please create more videos to explore more.
Thank you, I will
Excellent, Thank you very much
Thank you
wow. awesome summary,
Glad you liked it!
helpful👍
Sir, Ultimate Teaching Style, Sequence of arranging Topics are highly help full to us. Great
Thanks a lot. Please share with friends also.
Nice one thanks
Great video!!
Thanks for the visit
Liked it even before watching
Thanks Shubham.
great stuff, thanks
Great presentation and i think this is one of the best videos on simply making understandable to the concepts. thanks for the video
Glad it was helpful!
Great informative video. Thank you for sharing your knowledge.
Glad it was helpful!
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
Thanks a lot.
23:40
(80+42)/3 = 122/3 = 40.6
Thanks . I just subscribed
Explained well
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?
Can be framed in multiple ways
null can be “…it is 167” and alternative can be it is not, then you can prove or disprove null hypothesis
At Starting you said wrong because random Forest and decision tree can be used for both
Not sure which part of the video I said it. Both can be used for classification and regression scenarios.
awesome 👌
this is best I have seen ever
Thanks deelip. Pls share with friends.
Exceptional stuff.
Thank you. Pls share with friends as well.
Great. please keep up with e-commerce projects in ML practices. Ty
Sure , many thanks for appreciation and suggestion.
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
super useful
Thank you 🎉❤ excellent 👍
Welcome 😊
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
Sure thank you
That's very well explained highly appreciate the content ❤❤❤
Thanks again, please share with friends as well.
Great lecture.... 👌👍
Thanks a lot
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.
Really big thank you❤
You're welcome 😊
Thank you sir , cannu pls tell how to implement these in python
HI Pankaj, if you go to playlist section, you will find all the implementation as part of different playlists :)
Very helpful !
Glad it was helpful!
very well detailed great content
Much appreciated! your comments motivate me.
Really its amazing. Do you have any udemy course?
Thanks Robert, please check here www.unfolddatascience.com
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..
Definitely, thanks for suggesting, will do.
Thank you sir
Welcome
Excellent explanation
Glad it was helpful!
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
Noted
@@UnfoldDataScience thanks
Amazing video will let you know if I pass the interview 😂🙏🏼
Cheers, good luck
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!!
Thanks for your feedback.
Thank you so much sir
Most welcome
Thank you
You are welcome
You're making education engaging and accessible for everyone. #NurserytoVarsity
So nice of you. Please share with friends.
what is beta in logistic regression
?
coefiicients