Simplifying Confidence Intervals | confidence intervals | confidence intervals explained

[@rafibasha4145]

When will you start Transformers and LLms Aman ,also please let me know next quiz timing

[@alijaffry4179]

Imagine you have a big jar of marbles, and you want to know how many marbles are in the jar. You can't count all of them, so you take a small sample of marbles and count those instead. A confidence interval is a way to estimate the number of marbles in the jar based on the sample you counted. For example, if you take a sample of 100 marbles and count 50 red marbles, you can estimate that there are 500 red marbles in the jar. But you can't be 100% sure, because you didn't count all of the marbles. A confidence interval gives you a range of values that you can be reasonably sure the true number of red marbles is in. For example, a 95% confidence interval for the number of red marbles in the jar might be 450 to 550. This means that you can be 95% sure that the true number of red marbles is between 450 and 550.

[@siddharthvij9087]

Excellent topic for a vlog

[@nikhiliyer8436]

Eagerly waiting for your NLP playlist to finish Aman ,pls start Transformers BERt and LLmS

[@squadgang1678]

Yes please

[@Shadowmark-d1j]

Thank you. Can you explain degrees of freedom please

[@UnfoldDataScience]

kzbin.info/www/bejne/o4G4d6hnpq6mbLcsi=p65MTIEFnI_bgUwk

[@piyush6951]

Amazing video, just one question i have that is in hierarchical clustering and k-means clustering which works faster and why

[@Ankit-hs9nb]

K-means clustering is generally faster than hierarchical clustering, especially for larger datasets, because it involves fewer distance calculations and converges to a solution in fewer iterations.

[@sam02109]

Thank you so much sir Happy Pola from Maharashtra India 🎉

[@UnfoldDataScience]

Thanks a lot. Wish you the same.

[@brenoav99]

Great content as always, Aman. You had a good way of explaining the things. One thing is good to have the feeling is if we increase the CI we will increase the interval, what makes sense because we are increasing the range where the true mean is located, i.e., CI equals to 100% will be all the range (real numbers)