Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy

[@Cave_man_22]

You are the greatest teacher I ever had in my life. I always had questions about why this and why that but never found satisfied answers. But after watching your videos, everything feels like it should be what it is. You are really amazing, sir. You are the only one who satisfies my hunger for knowledge and what is happening behind the scenes. Hatsoff, sir. I wish one day I would meet you in person. Again, thank you very much for making such informative videos.

[@harkiratmakkar9202]

Absolutely Love your content sir. Just one correction. We minimise cross entropy because we add an extra minus sign. So, to maximise likelihood, we minimise the cross entropy. What you said is wrong that log of smaller number has higher value. Logarithm is a continuously increasing function, so to maximise f(x), you can maximise log(f(x)) which is equivalent to minimising -log(f(x))

[@siddheshkalgaonkar2752]

He has exactly said the same. Maybe you have misinterpreted.

[@waseemrandhawa5658]

First person on a KZbin whose teaching style is impressive. You are totally awesome. 😍 sir main ne You Tube pay probably 10 to 20 video likes ki hn gi. But aap ka jo teaching style hai majboor kar deta ha video ko like karne me aur dil say feelings ati hai "ja pana tussi great ho" 😇

[@bluestone2523]

same here

[@saiprashanth1587]

This is the best video on logistic regression and this is the best ML playlist in youtube. Thank you so much sir

[@binilrajadhikari2643]

This is one of the best tutorial about Logistic Regression in KZbin.

[@sanaayakurup5453]

I have never seen such a concise playlist on Logit. Thank you sir, all my doubts are solved!

[@arslanahmed1311]

We really appreciate the amount of effort you put into making this playlist

[@singnsoul6443]

I was looking for content to study machine learning for GATE DA. Thank you so much for, hands down, THE BEST content.

[@nitinchityal583]

Sir, you have made my machine-learning journey like a roller coaster... Thoroughly enjoyed

[@life3.088]

i have ur all on downlaoad due to net issue,but when i have net than i prefer to watch online .this is a small gift my side

[@priyanshutanwar]

The best explanation of cross entropy Ive come across!

[@HA-bj5ck]

Give this man a Medal!!!

[@purubhatnagar483]

you deserve an appreciation. best content i have read so far. you have my blessing. will buy your mentorship program too.

[@dakshbhatnagar]

Dhaansu Explanation, Bhai!! It's interesting to see how math is worked out.

[@abdulwahabkhan1086]

Yaar kamal k bandy ho aap!! Me jan'na chata hun k aap kitni mehnat krty ho ek video banany me.

[@aiml66_bangerashashankchan81]

The most goated Logistic regression explaination ever

[@manojssonawane]

Salute to you sir....The depth at which you are delivering the machine learning knowledge is very appreciable....Wish you a great fortune sir...

[@ParthivShah]

Thank You Sir.

[@akhilp6263]

This brought meaning to my life ❤🥂

[@tanujrana1212]

You are delivering much better content compared to some self proclaimed "leading provider of ML content".. Kudos..✌️✌️

[@anuragpandey5748]

best in depth explanation on internet 👍

[@PathakMoee]

Thank you sir

[@pranalipatle2215]

what a explanation .........felt clear& awesome

[@YashJaiswal-lr7vi]

Great Explaination. I want to add one correction: -1 < -0.04

[@Ravi-sl5ms]

Thank you. Wonderfully explained..

[@pritamrajbhar9504]

explanation was simple and on point.

[@Thebeautyoftheworld1111]

great work.Nice explanation!!!!!!God bless

[@anilkathayat1247]

Best Explanation Sir❤

[@rockykumarverma980]

Thank you so much sir🙏🙏🙏

[@MD.SAFANURISLAM]

Timestamps 00:02 - The perceptron algorithm failed to produce the optimal solution in logistic regression. 02:33 - In logistic regression, we need to find a loss function that minimizes the mistakes made by the model. 04:51 - The launch function helps determine the quality of a model and choose the best one. 07:37 - Calculating prediction probabilities and understanding the logistic regression model 10:20 - The Maximum Likelihood method calculates the product of probabilities for each data point to determine the better model. 13:01 - Replacing product with sum using logarithm 15:44 - Cross entropy is a key concept in logistic regression. 18:45 - The goal is to minimize the maximum likelihood in the binary cross entropy loss function. 21:14 - The formula used in logistic regression for binary cross entropy may not work for all cases 24:12 - Introduction to loss function and maximum likelihood 27:07 - Logistic Regression Loss Function and Maximum Likelihood

[@GauravKumarGupta-fn8pw]

Best explanation. thanks Sir

[@kalluriramakrishna5732]

Another level 💯

[@justcodeitbro1312]

wow thanks for this great explanation.

[@sahilvimal8795]

00:02 The perceptron algorithm failed to produce the optimal solution in logistic regression. 02:33 In logistic regression, we need to find a loss function that minimizes the mistakes made by the model. 04:51 The launch function helps determine the quality of a model and choose the best one. 07:37 Calculating prediction probabilities and understanding the logistic regression model 10:20 The Maximum Likelihood method calculates the product of probabilities for each data point to determine the better model. 13:01 Replacing product with sum using logarithm 15:44 Cross entropy is a key concept in logistic regression. 18:45 The goal is to minimize the maximum likelihood in the binary cross entropy loss function. 21:14 The formula used in logistic regression for binary cross entropy may not work for all cases 24:12 Introduction to loss function and maximum likelihood 27:07 Logistic Regression Loss Function and Maximum Likelihood Crafted by Merlin AI.

[@arslanahmed1311]

100% samajh aa gai

[@hasanrants]

thank you Sir, completed on 17th September 2024 @10:25PM

[@sujithsaikalakonda4863]

Great Explanation sir.

[@unkownboy9962]

why this channel is so underrated

[@rambaldotra2221]

Loved it Mind Blowing ✨

[@vrushabhjain1542]

Awesome explaination

[@Jayadev218]

Jai ho nithish sir GOAT

[@neelulalchandani7429]

best ever

[@AnjaliSharma-lq8ut]

I wish I could give more than 1 like.

[@dikshabatra8657]

Thankyou so much😄😄

[@adityabhatt04]

Great video.

[@MehulKumar-xw9rx]

Sir please Explain the mathematics behind the maximum likelihood formula in some new video

[@revathivamshi7621]

The Submission of negatives log of maximum likelihood is called cross entropy.

[@ajinkyahiwale5748]

23:58 How y2 = 0 and y4 = 0 ?? why u took the value as y2= 0

[@chetanshetty8368]

Hi Sir, Awesome videos. Can you please help me understand, when we are in a multi-dimensional space (Say 10 Independent and a Dependent variable(Binary)) how can we determine if the data is linearly separable. Thanks in advance

[@COMEDY_007..]

sir aakhir hum kyo perceptron LGA rhe hai jb hamare pass already sklrearn mai pre defined model hai😢😢

[@preetisrivastava1624]

Can we say that uncertainty is inversely proportional to probability ie entropy is inversely proportional to probability or log p or entropy is directly to -log p and then the solution lies in minimizing the loss or entropy or uncertainity

[@o2protectordm909]

Exercise book @CampusX

[@mohinigoyal3063]

Sir one more question Apne sabse pahle bola linearly separable Hona chahie data logistic regression banate samay Question hai ki hum Regression ko apply kiye bina check kaise karenge ki Vo linearly separable hai ya nahin means kya code hota hai Ya kase dekhe ki bo linearly sepreble hai ya nahi

[@anupamprasad8632]

Target variable me check karo ki binary classification hai ki nahi , multi classification me linear separable nahi hoga

[@astikpatel6522]

thank you sir

[@rafibasha4145]

How maximum likelihood and log loss const functions are related

[@mohinigoyal3063]

Sir please yah bataiye maximum likelihood ham logistic regression Mein kyon find Karte hai Kyunki linear relation mein ham sum of squared error nikalte Hain Jahan hamara error minimum hota hai to logistic regression Mein ham maximum error kyon nikalte Hain maximum likelihood se

[@saturdaywedssunday]

how to get that copy of one note what he has written. Did he upload it anywhere?

[@rajeshkr0021]

Sir Codes Download nhi ho pa rha github se and Datasets bhi. How to download?

[@shravanshukla5352]

Please upload a real application vedio of interpretation on logistics regression Suppose we analyze insurance data only 9% predict are eligible for personal loan

[@radhikawadhawan4235]

why did we consider product of probabilities to be Loss Function? in MSE minimising y - y_hat is logical, but i didnt get the logic for the same here.

[@abdulwahabkhan1086]

Thora sa probability ka game ha yahan. MSE me literal values le rhy hyn or usme probability nahi hay.

[@subhanjalpant8824]

22:00

[@ThanosAtWork]

do anyone have made notes of this deep learning playlist ?

[@ABHISHEKPRASADPRAJAPATI-jq3cg]

Teacher should have also teaching quality and your effort justify that....

[@ProgramerSalar]

gzb😀😃😄😄😄😄😄😄😄😄😄😄😄😄😄

[@kindaeasy9797]

maja aaya ++

[@RohanOxob]

9:00

[@jroamindia1754]

How y2 = 0 and y4 = 0 ?? why u took the value as y2= 0

[@ajinkyahiwale5748]

did you get the answer?

[@ajayraho]

Andrew Ng who? 😂

[@RohitkumarDanda]

xactly