Sir, Your a Gem , anyone can lecture but only few can teach and make class interesting ,my gratitude towards you will be forever ,thank You for this session . Please keep Teaching ,Mentoring . ❤🙏🙏🙏
Great masterpiece, rare to see a very smart and hardworking person in teaching, the best thing is you put a lot of effort in developing intuition, most teachers don't even know what intuition is
I have a small correction to make at 27:45, The formula is actually of PMF( Probability Mass Function) since Binomial Distribution is a discrete distribution, hence it is PMF and not PDF (Probability Density Function) which is used for continuous random variable.
Hi Sir, We have to do sample_means.std() * np.sqrt(50). That is why the error is occurring. Since the sampling std deviation is 1/sqrt(n) then actual standard deviation will be * sqrt(n).
Sir, you have used the entire population to calculate the sample mean which introduces bias in sampling. It would be better to do it on 40% sample to demonstrate the power of this theorem
I have a doubt that why at 1:30:32 we divided sample standard deviation with sqrt of sample size when the formula is sample s.d = (population s.d) / (sqrt of sample size).
While finding the interval there is no need to divide, because here the sample is normally distributed, so to find the interval we just need to use sample mean & sample sd, so the interval should be simply m-2sd ---- m+2sd, m = sample mean sd = sample sd
1:07:40 Why do sample means vary in case of uniform distribution? In uniform distribution, each point will have the same probability, so no matter whatever sample you create each sample will have the same sample mean which will be equal to the population mean, which will just create a single point, and that point will be the population mean in the sampling distribution of sample means isn't it? or I'm missing something here? Edit : after playing around with it I figured out that here we are talking about probabilities being same, but values are different, and our mean in this case is expected values, since values are different, their probability density will be same for each sample but sample mean will vary. this is why we will get the different sample means in sampling distribution.
1:50:07 I think chat me log ye puchh rahe the ki it should be Pop.mean / √n , but you have written sample.mean /√n. If sample mean is pop.mean/√n already then why would you divede SAMPLE.mean /√n? Which makes pop.mean/√n/√n twice divide by √n...
this lecture was fucking awesome. I understood confidence interval which was very difficult for me until now. Is there any way I can donate you some money? not by being a member on Paypal or something? please let me know.
Hello sir , I can't find any videos on uniform and poisson distribution of discrete probability distribution in your channel . Have you uploaded them or not ?? It is not in the maths for machine learning playlist
Hello sir, I am humbled by your gracious words and sincerely appreciate your kind feedback. Thank you for taking the time to express your appreciation of your teaching style and for letting me know that your lecture helped me gain a clearer understanding of the Central Limit Theorem. Your feedback motivates me to continue striving to provide high-quality educational resources Thank you once again for your positive feedback, and I wish you all the very best in your academic pursuits. 🙂🙏🙏🙏🙏🙏
you lectures will remembered as a masterpiece even after 10 years from now
Sir, Your a Gem , anyone can lecture but only few can teach and make class interesting ,my gratitude towards you will be forever ,thank You for this session .
Please keep Teaching ,Mentoring . ❤🙏🙏🙏
Great masterpiece, rare to see a very smart and hardworking person in teaching, the best thing is you put a lot of effort in developing intuition, most teachers don't even know what intuition is
I have a small correction to make at 27:45, The formula is actually of PMF( Probability Mass Function) since Binomial Distribution is a discrete distribution, hence it is PMF and not PDF (Probability Density Function) which is used for continuous random variable.
Hi Sir, We have to do sample_means.std() * np.sqrt(50). That is why the error is occurring. Since the sampling std deviation is 1/sqrt(n) then actual standard deviation will be * sqrt(n).
Right
itne easily koi nahi samjah sakta... u r a miracle🙏🙏🙏🙏
this is pure gold and now i m becoming a member
Sir please 😊 bring videos on Time series analysis using ARIMA and SARIMAX Models
each and every point is clear sir. Thank you sirji.
Thank you for this amazing session! You are really a best teacher!
Sir this is just incredible! What great content really sir !
Sir, you have used the entire population to calculate the sample mean which introduces bias in sampling. It would be better to do it on 40% sample to demonstrate the power of this theorem
I have a doubt that why at 1:30:32 we divided sample standard deviation with sqrt of sample size when the formula is sample s.d = (population s.d) / (sqrt of sample size).
While finding the interval there is no need to divide, because here the sample is normally distributed, so to find the interval we just need to use sample mean & sample sd, so the interval should be simply m-2sd ---- m+2sd,
m = sample mean
sd = sample sd
@@AmarSharma60436 bro that is what he is saying, like either you do pop.SD / √n. Or. Just do sample_means.SD.
Happy Guru Purnima sir ji ❣🙏
1:07:40 Why do sample means vary in case of uniform distribution? In uniform distribution, each point will have the same probability, so no matter whatever sample you create each sample will have the same sample mean which will be equal to the population mean, which will just create a single point, and that point will be the population mean in the sampling distribution of sample means isn't it? or I'm missing something here?
Edit : after playing around with it I figured out that here we are talking about probabilities being same, but values are different, and our mean in this case is expected values, since values are different, their probability density will be same for each sample but sample mean will vary. this is why we will get the different sample means in sampling distribution.
I was also having this question (confusion), thanks for solving it. :)
thank you sir🙏🙏 for making mathematics for machine learning easy
sir love you from pakistan sir you're truy gems for machine learning students sir tysm for provide us this content tysm sir
Best teacher ever.😊
Sir really CLT outstanding explanation tha pura hi visualise hua🤜🏻🤛🏻
1:50:07 I think chat me log ye puchh rahe the ki it should be
Pop.mean / √n , but you have written sample.mean /√n.
If sample mean is pop.mean/√n already then why would you divede SAMPLE.mean /√n? Which makes pop.mean/√n/√n twice divide by √n...
Thank You Very Much Sir.
this lecture was fucking awesome. I understood confidence interval which was very difficult for me until now. Is there any way I can donate you some money? not by being a member on Paypal or something? please let me know.
this blows my mind, thanks sir😇
such a excellent explanation
Amazing power of central limit theroerm
Hello sir , I can't find any videos on uniform and poisson distribution of discrete probability distribution in your channel . Have you uploaded them or not ?? It is not in the maths for machine learning playlist
Hello sir,
I am humbled by your gracious words and sincerely appreciate your kind feedback. Thank you for taking the time to express your appreciation of your teaching style and for letting me know that your lecture helped me gain a clearer understanding of the Central Limit Theorem.
Your feedback motivates me to continue striving to provide high-quality educational resources
Thank you once again for your positive feedback, and I wish you all the very best in your academic pursuits. 🙂🙏🙏🙏🙏🙏
ye chatgpt se likha hai na
woooow
this was sooooooooooooooo good
20:50 The probability of getting like on your video is 1 from my end.
Can you take classes on linear algebra?
too good:)
Please share pdf notes previous and yesterday class sir
Session 43 Notebook pdf link updated.
@@sumankumarsuman2656 please share 42 session notebook also