Random Variable, Probability Mass function, Cumulative distribution function|PMF and CDF

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Random Variable, Probability Mass function, Cumulative distribution function|PMF and CDF
#RandomVariable #probabiltiymassfunction #unfolddatascience
Hi, my name is Aman and I am a data scientist.
In this video, I discuss about random variable, probability mass function and probability distribution function. I talk with example what is PDF, PMF and CDF. Below questions are answered in this video:
1. What is a random variable
2. What is Continuous and discrete random variable
3. What is Probability mass function
4. What is Probability distribution function
5. What is Cumulative distribution function
About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
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Опубликовано:

7 сен 2024

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Комментарии : 42

@Ramesh-rp6jq 4 года назад

Your teaching style is unique which anyone can easily understand the concepts. Now I am clear on pmf with cdf as part of Probability distribution. Looking for same concept on real time scenario where it can be useful in DS

@UnfoldDataScience 4 года назад

Sure Ramesh. Thank you :)

@haweyorashid9419 2 года назад

I would have considered myself a smart person if you were my teacher b/c I am understanding everything so easily while I am loss in stupid lectures thank you so much for sharing your beautiful knowledge

@UnfoldDataScience 2 года назад

Thanks a lot.

@joylethabo6417 Год назад

10/10!! where has this channel been hiding this whole time 😭

@UnfoldDataScience Год назад

Thanks you. Please share with friends as well

@sandipansarkar9211 2 года назад

finished watching

@anaya1012 2 года назад

Thank you so much sir 🙏🙏🙏🙏

@UnfoldDataScience 2 года назад

Welcome Anaya

@mikehynz 5 месяцев назад

I love your videos, thank you.

@UnfoldDataScience 5 месяцев назад

Your words mean a lot to me

@mosama22 2 года назад

Really, thank you for the BEAUTIFUL explanation man, really appreciate :-)

@UnfoldDataScience 2 года назад

Welcome.

@farhadkhan3893 2 года назад

very easy method.. I got it,, love from Pakistan

@UnfoldDataScience 2 года назад

Thanks Farhad.

@preranatiwary7690 4 года назад

Good one as always

@UnfoldDataScience 4 года назад

Thanks for the visit :)

@sadhnarai8757 4 года назад

Very good Aman

@UnfoldDataScience 4 года назад

Thank you.

@AJ-et3vf 2 года назад

Awesome video sir! Thank you!

@UnfoldDataScience 2 года назад

Thanks for your positive feedback. Please share with others as well who could be benefited from such content.

@md.alamintalukder3261 Год назад

Thanks a lot

@ajaynimmala2494 3 года назад

P(x=1) is 3/4 as we have three cases where we can have atleast one Head in our random experiment as per our X in discussion. Please clarify?

@UnfoldDataScience 3 года назад

Yes True.

@souravbiswas6892 4 года назад

Neat and clean representation 👍

@UnfoldDataScience 4 года назад

Glad you liked it Sourav.

@UnfoldDataScience 4 года назад

Glad you liked it Sourav.

@YashpalNSharma 4 года назад

Hi Aman - In real time Machine Learning, how often do we find data which does NOT follow a normal distribution (bell curve - right or left skewed)? Just trying to understand how data distributions are in general practical use cases. Thank you!

@UnfoldDataScience 4 года назад

Hi Yashpal, generally with real data we do not see normal distribution often. However there are many ways to take care of skewed data.

@YashpalNSharma 4 года назад

Unfold Data Science This makes these videos all the more important. Looking forward to this playlist covering multiple distribution types.

@krittikamaheshbabu4379 Год назад

in cumulative distribution function , p(x greater than or equal to 1 ) is also 3/4 right?

@xoda345 2 года назад

i did not understand how in the coin tossing case, for a random variable it is 0 if the outcome is head and 1 if the outcome is tail? Should not it be 1/2 in both case?

@jaxayprajapati5597 4 года назад

your content is very helpful but i can not use this concept with real dataset so plz can you take real dataset

@UnfoldDataScience 4 года назад

Hi , thanks for your words. I will use this concept with real data as well :)

@ashokthulluri4748 3 года назад

sir, how it's take p(x

@UnfoldDataScience 3 года назад

Hi Ashok, This one I have explained little bit in calculus video, same concepts apply here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-WCp1D-wSolo.html

@jaxayprajapati5597 4 года назад

and what is random variable for dataset

@UnfoldDataScience 4 года назад

Hi Jaxay, It depends on which distribution we are talking about. For example - Bernoulli, uniform etc

@jaxayprajapati5597 4 года назад

plz aap real dataset leke ye concept samajaye

@UnfoldDataScience 4 года назад

Sure