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Amazing coverage of statistics. Please make a video over mathematics required in data science. I am very confused like what to cover and how much to cover in mathematics.
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Mam I am new to learn data science what is the sequence of concept which I have to follow and is any prerequisite required before this Or if I randomly learning anything for this is good or bad ??? Please mam ????
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Hi Ahmad, our references are collective and if you wish to use them for your practise. Kindly drop in your respective email id and we will share it to you. Cheers :)
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We are very glad to hear that your a learning well with our contents 😊 continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
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At around 7:58 minutes, you have said that Spam or not Spam is considered as Discrete or Categorical .. so does it not come under Nominal Data in Qualitative??
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A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class.
Hi, unfortunately we do not share the presentations used in our tutorial. On the bright side, here is a link: bit.ly/39ZZ7lh to help you. Happy learning :)
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So far the best source available on web to brush up stats concepts for Data Science.
Cheers to Edureka & Zulaikha.
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This is Pure Gold
Thank You 😊 Glad you liked it!!
Great Explanation on the IG and Entropy part. Thanks for that!
Amazing coverage of statistics.
Please make a video over mathematics required in data science. I am very confused like what to cover and how much to cover in mathematics.
Thanks for the compliment, Abhishek! We're glad you liked it. We shall forward your request to the content team.
Excellent explanation, thank you for making such a video which explains the concepts so clearly
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Thank you so much for making video on complete probability , statistics in one video
You are most welcome 😊 Glad it was helpful!
It is humble request if you will share pdf of this video ?
You people have done a great, amazing work
Thanks
The way she is teaching 👌
Awesome, it clear my all doubt🤟
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I had been trying to understand IG and entropy but never got such a simple explanation. Thanks very much Edureka team :)🙂🙂
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thank you so much for sharing this video😇😇
explanation was very good, thank you
Mam I am new to learn data science what is the sequence of concept which I have to follow and is any prerequisite required before this
Or if I randomly learning anything for this is good or bad ???
Please mam ????
Excellent explanation! Thank you.
Your voice is really good. It is so sweet just like needed for the students to capture their attention on the subject matter
Thanks.
😇😇😇
Sure 😇😇😇
Thank you Zulekha from EDUREKA.
Good Evening ma'am thank you for awesome video and Your voice like Madhuri Dixit. lovely voice.
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
Amazing Video! I really tried answering the question for Bayes Theorem but couldnt figure it out... Could you help me out please?
INTRESTING & KNOWLEDGEFUL and stepping stone to SUCCESS.
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THANKS FOR THE QUALITY VIDEO AND MINE OF INFORMATION 👍🏼
superb!!well Explanation
Glad it was helpful!
❤ Great job Edureka !
This was really helpful!! Thanks for uploading!!
You are welcome👍
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Good presentation thx
concise and informative...thank u so much...Edureka team.
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@@edurekaIN already subscribbed..
Thanks EDUREKA.. .. .. .. ..
Good presentation and content
Thank you for this
Superb session, Thanks to Edureka and trainer, crisp precise and well modulated - where can I get a copy of this presentation for reference?
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Thanks a lot.. Mam and Edureka team.. Very good content.. And.. Nice explaination by mam..
You are welcome Shivam. Don't forget to Subscribe our channel.
Thank you Zulekha and Edureka
Awesome thanks
Thx for sharing
I love your voice 😍❤
so nice of you
superb!!!
thank you for this wonderful video, but may i ask what reference you use?
Hi Ahmad, our references are collective and if you wish to use them for your practise. Kindly drop in your respective email id and we will share it to you. Cheers :)
Very nice explanation...thank you...
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thanks a lot, i learned a lot i did not know
Amazing training! Lol the bowl sounded like bowel....regardless best training!
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MAM explained it very nicely
*35:05*
Thank you so much mam
Please upload video about confidence interval
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@@edurekaIN thank you
it goes fast although, but good teaching
At around 7:58 minutes, you have said that Spam or not Spam is considered as Discrete or Categorical .. so does it not come under Nominal Data in Qualitative??
It is Quantitative.
Very nice explanation
excellent video. can i get the material or notes of this topic. I am student
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solution of probability question is 5/36 i think
Awesome session 👍
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why are true positive and false negatives considered as classification?
A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class.
A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class.
Informative video
nice work
Nice vedio.
Thank you so much
You are welcome👍
Where can I find the theoretical material for the same?
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could you share this slide please?
Hi, unfortunately we do not share the presentations used in our tutorial. On the bright side, here is a link: bit.ly/39ZZ7lh to help you. Happy learning :)
Your voice is cherry on cake. Outstanding .
can you please send me the ppt ?
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pdf provide kra do please
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i like to
Great
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Please in hindi
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answer is 4/5
Thanks for the lecture
Most welcome!