Probability Vs Likelihood | Difference between Probability and Likelihood

[@sreenathpgc]

I have gone through many videos for maximum likelihood for the last 24 hrs, you gave the excellent explanation with the example to make me understand the concept. Thank you brother.

[@warnerferraz1185]

Mesej yang jelas, struktur yang jelas, mudah difahami, terima kasih

[@abdullahhadi5450]

out of other videos regarding this topic, it feels like your video clearly explains the objective that likelihood and probability achieve. I rarely comment, and when I comment on something then the video explanation is awesome 🔥

[@md.aamirsohail7133]

Good explanation.

[@gajendra1987]

amazing explanation, hats off

[@carlscaglione2022]

Nice. Clear explanation because the distinction between being PDF-driven vs. event-driven or situation-driven is a crucial distinction

[@morganvhulenda]

Helpful, thanks

[@UnfoldDataScience]

Thank you.

[@utkarshsingh2675]

nice video sir...thanks

[@dhanushreeyadav1442]

very understandabel explanation sir thank u very much 🤝

[@spaceadvanture6458]

You explained Better than my so called paid certification course

[@HarshaJK]

Thank you for explaining this concept so well. I am very clear of this topic now.

[@UnfoldDataScience]

Glad it was helpful!

[@arshdeep9355]

best explanation

[@shooter5439]

great explanation

[@faizulislam9047]

Thank you so much. Your explanation is outstanding!

[@theplayer2647]

Thankyou very much sir 🙏🙏🙏 I am struggling from last 5 days for this

[@UnfoldDataScience]

Welcome.

[@devirajfly]

Very clear explanation ! Thank you

[@kmishy]

this video gave me a perfect explanation

[@RobertWF42]

I learned likelihood as L(mu, sigma | x) for a single value of x rather than a range of values, which is equal to the density. For a range of x values x_0 < x < x_n the likelihood is then equivalent to the probability, correct?

[@naveensolanki9054]

No, the likelihood and the probability are not equivalent, even for a range of values of x.

[@sylwiagotzman5422]

It is probability either way, just it is called likelihood in one of the cases.

[@prateeksachdeva1611]

very nicely explained

[@UnfoldDataScience]

Thanks Prateek. Pls share with friends

[@sangeethag9749]

Inference : Difference between the Probability & Likelihood! Probability corresponds to finding the chance of something given a sample distribution of the data, while on the other hand, Likelihood refers to finding the best distribution of the data given a particular value of some feature or some situation in the data. Example of Probability! Consider a dataset containing the heights of the people of a particular country. Let’s say the mean of the data is 170 & the standard deviation is 3.5. When Probability has to be calculated of any situation using this dataset, then the dataset features will be constant i.e. mean & standard deviation of the dataset will be constant, they will not be altered. Let’s say the probability of height > 170 cm has to be calculated for a random record in the dataset, then that will be calculated using the information shown below: Calculating Probability In the above, “mu” represents mean & “sigma” represents Standard Deviation. While calculating probability, feature value can be varied, but the characteristics(mean & Standard Deviation) of the data distribution cannot be altered. If in the same dataset, the probability of height > 190 cm has to be calculated, then in the above equation, only the height part would have changed. Example of Likelihood! Likelihood calculation involves calculating the best distribution or best characteristics of data given a particular feature value or situation. Consider the exactly same dataset example as provided above for probability, if their likelihood of height > 170 cm has to be calculated then it will be done using the information shown below: Likelihood calculation In the calculation of the Likelihood, the equation of the conditional probability flips as compared to the equation in the probability calculation. Here, the dataset features will be varied, i.e. Mean & Standard Deviation of the dataset will be varied in order to get the maximum likelihood for height > 170 cm. The likelihood in very simple terms means to increase the chances of a particular situation to happen/occur by varying the characteristics of the dataset distribution.

[@thesirsaurabh]

Jio guru

[@enerz9135]

Hindi Bolta to jayad Maza aata bhai Leaving Your Video now

[@KishanKumar-cr8hs]

very nice explanation sir

[@UnfoldDataScience]

Thank you

[@giraganipallavi4594]

thq very much sir crystal clear explaination

[@UnfoldDataScience]

Thanks Pallavi.

[@dodolookr]

very clear ， thanks

[@javid659]

Very well explained dear. Thank you.

[@UnfoldDataScience]

Thanks Javid 🙂

[@mehedeehassan208]

7:03 oped a new world for me ... thanks ..

[@UnfoldDataScience]

Welcome Mehedee.

[@mehedeehassan208]

Do you have kaggle profile.??.just want to follow and learn

[@mahsaaa534]

Thank you so much for this video. the only video that helped me understand this today. You are amazing!

[@UnfoldDataScience]

Most welcome.

[@dd1278]

Very clearly explained.

[@UnfoldDataScience]

Glad it was helpful!

[@life_inked]

You relieved the burden

[@santanuroy1351]

Is likelihood is also a probability distribution?

[@sandipansarkar9211]

finished watching

[@igudiaosarumwense6925]

Please, how can I get materials on Minimum likelihood.

[@salonisingh6032]

WAITING FOR MORE VIDEOS IN DATA SCIENCE. PLS SIR

[@UnfoldDataScience]

Hi Saloni, you can visit playlists and watch all videos of your choice. www.youtube.com/@UnfoldDataScience/playlists

[@sudipnarayanchoudhury1112]

Pls cover Degrees of freedom..

[@UnfoldDataScience]

Already created, search with "topic name + unfold data science"

[@piyalikarmakar5979]

Thank you so much sir for this vedio.. Kindly do more related vedios on estimations.

[@UnfoldDataScience]

Sure. Thank you.

[@moopoo123]

Thank you. Very clear explanation.

[@nawfelnawfel4950]

Very clear like always thanks a lot

[@archanamaurya89]

Hi, great video! Is there also a video on maximum likelihood estimation? If not, could you PLEASEEEE make one or refer a good article for me? Thanks much!

[@kingoftime1114]

Is this comple playlist is enough Statics used in data science???

[@UnfoldDataScience]

No, go to playlist ans watch more videos

[@javid659]

If you plz hint, what we can assume y-axis values.

[@rohitbhosale4614]

Great! Sir, please cover " degrees of fredom".

[@UnfoldDataScience]

Sure Rohit.

[@akashkumar-bq7cl]

Excellent and clear!but at what scenarios in the data science life cycle do we implement this?

[@UnfoldDataScience]

Thanks Akash. Will tell that part as well.

[@mlbasics6267]

@@UnfoldDataScience Thank you Aman, as akash requested could you please share some real world example it will become even more clear

[@makting009]

I think likelihood is mainly used in loss functions and parameters are used to draw that pdf.

[@Anonymous00tttt]

Sir your concept is very good can you explain the latest version spss 29.0 bayesian output and likelihood curves in a seperate video

[@hybriddude007]

The likelihood of an Indian speaking in a funny way is 100, the probability that an Indian speaks in a non-funny way is 100%

[@UnfoldDataScience]

Thanks for feedback 😅

[@sayanmondal3876]

You are really a great teacher!! I wish you could be my mentor in the company.

[@UnfoldDataScience]

Thanks Sayan.

[@piero8284]

My big question is, is it common in machine learning papers to find this concept used in a interchangeably way?

[@UnfoldDataScience]

Sometimes yes

[@shekhy123]

Thanks a lot. I was not getting it.

[@UnfoldDataScience]

Cheers Abhishek

[@adityaarvind6945]

Sir can you please explain the difference between validation set and test set

[@UnfoldDataScience]

Validation set to tune parameters of model Test set to find performance of model on unseen data

[@makting009]

More data science maths videos

[@UnfoldDataScience]

Sure Sunreet.

[@kingoftime1114]

@@UnfoldDataScience is this playlist is is enought for data science maths portion?

[@kumarsamyak513]

what is log likelihood?

[@himanshumangoli6708]

Sir can you please explain this topic on Naive bayes handling numeric features

[@UnfoldDataScience]

Noted Himanshu.

[@kavitakushwah4908]

Didn't get