Session 40 - Probability Distribution Functions - PDF, PMF & CDF | DSMP 2023

[@rauhan_sheikh]

sir you are one of the best teachers please continue teaching and bring deep learning course as well after this.

[@AltafAnsari-tf9nl]

Statistics was never easy before watching this video 🙌

[@THEDATAVED]

can we declare him the best in this domain

[@anuragagarwal4680]

he is sharukh khan in statics domain, his teaching style , just wow

[@PraveenKumar-vd8ev]

Yes definitely agreed
😃😃😃

[@atharvshivale5843]

No

[@asifalikhan580]

No doubt at all

[@ArunMaurya-q9c]

why not

[@VikasSingh-o2b]

Real teacher by all means.. He is doing excellent in this domain. A man with perfection. ❤

[@rajkumarpanditYouTub]

Sir, aap Indian student k samajhane k tarike KO achchhe se samajhte ho❤

[@SuperRia33]

Nobody has covered it so extensively in depth ..very insightful and eye opener session

[@siddheshdhanawade3709]

the man has got balls for data science

[@santanusarangi]

You are just great, your teaching style is very good

[@M0h1tKum4r]

Because of nitish sir, I can be a successful data scientist. May god give him a lot of success in his life.

[@rajgurubhosale8680]

thank god finally someone explained in detail
why we need distribituions of data function

[@LastLife-AnujModani]

00:12 - Introduction to new statistics concepts
03:33 - Understanding Probability Distribution Functions
05:32 - Understanding random variables and their significance in probability theory.
06:29 - Understanding of random variables in probability distribution functions.
08:34 - Understanding Discrete Random Variables
09:26 - Understanding Probability Distribution Functions
11:25 - Understanding Probability Distribution Functions
13:43 - The video discusses probability distribution functions.
18:25 - Understanding Probability Distribution Functions
19:31 - Creating mathematical relationships for modeling outcomes and probabilities.
21:41 - Understanding Probability Distribution Functions
22:48 - Introduction to Probability Distribution Functions
25:10 - Understanding Probability Distribution Functions (PDF)
25:59 - Probability Distribution Functions in detail
27:57 - Probability distribution functions help understand the spread and likelihood of outcomes.
28:50 - Understanding data distribution through visual representation
30:22 - Applying knowledge of uniform distribution to benefit data analysis
30:59 - Discussed Probability Distribution Function parameters
32:38 - Understanding Probability Distribution Functions
33:46 - Probability Distribution Functions in Mathematics
37:07 - Understanding Probability Density Functions (PDF) and Cumulative Distribution Functions (CDF)
38:32 - Probability Distribution Functions
42:39 - Introduction to Probability Distribution Functions
45:23 - Understanding Probability Distribution Functions
47:54 - Introduction to Probability Distribution Functions (PDF, PMF & CDF)
49:27 - Introduction to Binomial Distribution
52:10 - Understanding Probability Distribution Functions (PDF)
54:09 - Understanding probability distribution concepts.
59:31 - Understanding PDF, PMF & CDF in Probability Distribution Functions
1:01:43 - Probability Density Function (PDF) is a mathematical function to describe probability.
1:05:15 - Probability density functions explained
1:07:01 - Understanding probability density functions
1:09:50 - Probability density function calculates probability between two values.
1:11:07 - Introduction to Probability Distribution Functions (PDF, PMF & CDF)
1:15:45 - Introduction to Normal Distribution
1:17:06 - Understanding Probability Distribution Functions
1:20:16 - Understanding Parametric and Non-parametric Density Estimation
1:21:07 - Understanding density estimation and probability distribution functions
1:22:51 - Parameter estimation using probability density function
1:23:53 - Understand Probability Distribution Functions
1:27:04 - Understanding Normal Distribution
1:28:21 - Understanding Normal Distribution Equation
1:31:33 - Understanding Probability Distribution Functions
1:34:01 - Discussing population parameters estimation with variations
1:36:09 - Understanding Parametric Probability Distribution Functions
1:37:57 - Understanding non-parametric density estimation
1:39:43 - Understanding nonparametric density estimation
1:40:30 - Non-parametric techniques like Kernel Density Estimation are useful with limited data points
1:43:12 - Understanding Normal Distribution Parameters
1:45:28 - Understanding the mean of a distribution and its dependence on standard deviation.
1:47:56 - Understanding Kernel Density Estimation
1:48:43 - Probability distribution functions are used to model data distribution.
1:50:52 - Discussing the calculation of probability distribution functions
1:52:05 - Calculating probability distribution functions for normalization.
1:57:42 - Understanding continuous random variables and creating PDFs
1:59:56 - Understanding probability distribution functions
2:02:37 - Explaining Probability Density Functions in detail
2:03:36 - Understanding Probability Distribution Functions
2:05:27 - Understanding Probability Distribution Functions
2:06:35 - Understanding representation of data through histograms and sample data
00

[@dilipgyawali1776]

wow really great explanation in a simple way to make the object crystal clear .....u are really a awesome mentor ...thank u very much.

[@Abhishek-qw6ny]

sir i have no words to express about your contact that way you studied about sir 1 day you become recognised by every asparint which come in data

[@shripaddeshpande5766]

Beautiful explanation of Density Estimation. Fantastic session as usual. Just one correction - Poisson is a discrete distribution, not a continuous.

[@preethibs1758]

Awesome video which covers all the main topics , explained very easily.

[@oofyxl4632]

You are highly underrated. I have seen other lectures of pdf and understood 1% of what you taught in one video. Thanks from Bangladesh ❤.

[@Ram_jagat]

Sir you really are a master when it comes to explaining complex terms into simpler forms.. hats off sir...

[@sagarchaudhary97610]

Hello bro!

[@mdashrafulalam6510]

Thanks a ton. Undoubtedly the best explanation about these topics we have on the TH-cam

[@rushikesh8132]

Wow ! What a great lecture !

[@sreyag4766]

The way you explained the difference between probability and probability density usage in PDF is awesome sir!

[@subinsudhakar8521]

Thank you for the detail explanation and keeping it simple.

[@pavanKUMAR-tt4jt]

sir you are the best teacher i have ever seen

[@gauravpaithane]

Superb Explanation

[@saifalimuhammadi2147]

One of the best explanation of this topic. Nobody can beat this guy of content

[@omparghale]

Phenomenal explanation.
Thank you!!

[@ghostofuchiha8124]

This is by far his most informative and complicated video

[@asadabbassheikh]

Give your insta id. I want some help from you.

[@bhaviksuiya6600]

This is best explanation literally i search everywhere for what and how data distribution is used in ML but i only find theory here i actually able to understand how it data distribution look how we interpret it just loved it .... Even i am doing masters this is very helpful and easy to understand :)
Also suggestion please break this live session in small video so you get more views also our time also saved bcz this video is not recommended me i am just lucky to find it :)

[@aksharasharma1469]

Amazing coursee very very helpful specially for beginners❤❤

[@homeboy1733]

Sir go into as much depth as you can, audience will adjust.

[@arindamInsightFullMath]

1:07:50 there is a simple correction: if u use the limit concept at that point we can get the point we can get the number and that number is going to be the probability of that given point .
Example:if we have to count the probability of stock price at every possible point which is present in the real line under a given interval of time the all time probability is not going to be zero . Thank u sir ..

[@junaidrahat35]

Dear sir you are doing a great job.fantastic work.👍

[@anniedhawan7945]

Amazingly explained!!!!!!

[@mirnadeem752]

great way of learning keep shining and rising sir🙂

[@chandank5266]

if explaining complex term is an art, Nitish Sir is the SUPREME artist👽

[@milindtakate5987]

Can you create video on shap values and their use in interpreting machine learning models

[@ashutoshpatidar3288]

Such a high quality content❤ they don't even teach in such highly paid batches

[@nomannosher8928]

I think, I should forward this lecture to my course instructor😃

[@ParthivShah]

Thank You Very Much Sir.

[@shashankshekharsingh9336]

thanks you so much sir🙏🙏 , no one can explain like you

[@deepakarya4584]

This man is a legend

[@talibdaryabi9434]

thank you so much,

[@timetraveller7513]

48:10 the condition says the sum of probabilities assigned should be 1, however in the case of 2 dice won't it change as probabilities for 7 will be highest whereas 2 and 12 is lowest

[@దావీదురాజు-ధ5బ]

At 1:30:32 why we are creating "values" (using sample's min and max values) instead of using the available sample to calculate probabilities?

[@balrajprajesh6473]

Sir you the best

[@khemchand494]

At 1:17:08, you have assumed Poission distribution as pdf but, since it is specifically for discrete random variable, its pmf should have have been displayed

[@fullthrottlevishal]

Hi Nitish sir , aaap aese hi video bante jana , mark my word campus X content is just too awesome , maine krish ji ka bhi sara video dekha hai , and statquest ka bhi stats ke liye sabse best playlist hai , time series analysis ke upar bhi sir video lekar aooo it will be really help full

[@saifalimuhammadi2147]

Thank you sir python pe dikhane ke liye

[@Mon_isha09]

great explanantion

[@lego-xq4fh]

You're the best

[@asifkhan-rb8tl]

Clear

[@INGLERAJKAMALRAJENDRA]

Refer to GPT's Exponential vs Uniform distribution chat for more clarity and notes on Probability density

[@mdriad4521]

Sir if you have taken any batch classe on deep learning before you can upload those vidoes.Because we got stuck in deep learning and nlp.

[@uday7016]

Yes he has check his playlist on deep learning bro

[@mdriad4521]

@@uday7016 ok

[@uday7016]

@@mdriad4521 have u subscribed to sir course?

[@mdriad4521]

@@uday7016 No

[@swap5077]

Nice look

[@suhaibdata]

thank u

[@sadiazaman7903]

is poisson distribution is PMF function or PDF function. According to the given graphs in 4th slides it seems poisson lies in PMF but later on in slide 8 and 10, you have used it in PDF. What is the reason behind it. please make it clear. Thank you

[@manishKapall]

clear

[@Leerical-p9s]

Thanks for lecture. But please give daily lectures atleast

[@rauhan_sheikh]

everyone is not like you, he also has to prepare material for us and prepare task and solutions and also has a life. For students as well everyone is not just a student people have jobs. 4 classes a week is fine.

[@Leerical-p9s]

​@@rauhan_sheikh this way it will take lot more time than we think . Good content needs to go through our concepts regularly for learning. People wants to apply for jobs as soon as they feel confident in overall concepts and learning. Regular lecture will help us more I think.

[@rauhan_sheikh]

@@Leerical-p9s Can you please think of others as well? Why so self centred? He has said this multiple time that there's a lot of work behind the scenes but apparently you only care about your regular videos. 🙂

[@uday7016]

@@rauhan_sheikh Have u subscribed to sir's course?

[@rauhan_sheikh]

@@uday7016 yes!

[@nss55]

You are videos are very clear and ueful but teach in english

[@wewesusui9528]

Bro just made a degree level course for data science

[@pankajverma5716]

Sir, in the code of KDE why are we fitting the data? In theory there was no mention of fitting the data.

[@somyaagarwal2942]

Where were you all my btech time sir 😭✨

[@Divyani05]

Sir you haven't told why there is n-1 in sample std or variance 's formulae

[@CodeWonders_]

Hii sir can you teach web scraping in detail for freelancing I saw your previous web scraping video but it's not enough

[@ankitbiswas8380]

Sir in the end you answered a question where you said that histogram is not reliable . If we have a sample that is not accurately representative of the data , then the histogram will not work and PDF helps there. But our pdf is calculated after fitting our sample to either parametric function or non parametric function like kernel density . So how will pdf be any better in that case?

[@ECSumit]

Brother! have you got the answer?

[@zainabad5081]

Does calculating probability density is important to learn for a data scientist? I don't think so, he should know about pdf,cdf,pmf but calculating is a part of deep statistics. Isn't it?

[@AryanKulkarni-td9zp]

1:18:07

[@shubhamvishal1216]

43:00

[@sriranjanisrinivasan6649]

HI sir at 1 hr:28 mins, the sample is having 1000 values right? SO the population is having more than sample is it?

[@rohinisingh6916]

Yes

[@adnanhaider1629]

sir ye lecture data Analytics k lye hai ya machine learning k lye

[@rushikesh8132]

Statistics is needed for data analytics as well so yes it is very useful for both .

[@rehanahmed7072]

Hello sir I am from Pakistan I want enroll for dsmp how can i enroll ?

[@KumR]

1.5

[@nasreenmohsin]

one thing he not explain well Student Ask Question why we Devide by Total Number of Sum Ans: becuase we took Ratio so that bar Y Value remain in Ration not exceed up to 10000 if we dont dived Plot result will be the same please check and update me

[@pradnyanandbhadarge1552]

Poisson distribution is PDM function Correction !

[@cryptonews6565]

yes

[@shubhamvishal1216]

1:25:55

[@shubhamvishal1216]

1:48:57

[@ekemm]

1:41:12

[@Samtoosoon]

1:06:27