I had to reupload this video because I forgot to include the part on ensemble techniques due to an editing error in the previous upload. Check timestamps.
I am writing to request your assistance in creating videos that delve into metaheuristic approaches, such as genetic algorithms, ant colony optimization, and others. It has come to my attention that there is a noticeable scarcity of resources covering these topics on platforms like TH-cam.
Awesome explaining!!! I'm not just talking about one video-the entire ML playlist is AWESOME! After consistently dedicating around 5 months to this playlist, I now feel confident in my ML skills. A big thanks to @Nitish Singh for this incredible journey!
Hi Sir as i have finished you 100 days ml playlist what can be our next step, please guide, Thank You Sir for your great playlist and Great Explanation.
Thanks sir for this beautiful playlist. Never have i ever thought i would be able to understand all concepts of ml so easily. i am really grateful to you. i just completed this playlist after grinding for around 2 months. I have been going with some problems in my life that i am fighting but i will come out stronger now that I have completed this, soon I will start the DL playlist too and complete it too. Thank you for everthing sir.
I am Shumail Malik, a PhD student at Hanyang University, South Korea. I have seen you transition from a young person to an older one over these 100 days of Machine Learning. Without a doubt, you have done great work for the public! Your teaching style and accent are truly impressive, and your simplicity in personality is very attractive. Thank you very much, Sir.
I am writing to request your assistance in creating videos that delve into metaheuristic approaches, such as genetic algorithms, ant colony optimization, and others. It has come to my attention that there is a noticeable scarcity of resources covering these topics on platforms like TH-cam.
Sir, I truly admire your work and love all of your videos, learning so much from them. Thank you!!! I have one question: at the end of the video you said that in spam filtering false positive is the critical one but if one msg is spam and classified as not spam(false negative) that will be the critical case isn't it? false negatives are generally considered to be more dangerous in this case because they can expose the recipient to potential harm.
I think false positive is more critical because it may send your important mail in spam which is more harmful rather than showing some spam mails as important mail.
i had this in an interview recently and i fkd up a bit :( They gave me a dataset and were expecting how to handle such type of data and make it soo that the predictions that we make are reliable
Hi Sir, Big Fan!! I was searching for class imbalance video and you have uploaded it on right time. I am training an ANN model for customer churn prediction where my dataset has class imbalance issues 96:4. I have used Upsampling, Downsampling, SMOTE, SMOTE-ENN, Class Weight but neither of them gave promising results and fail to predict well on minority class the recall value is very low. What should be done in such case where the model is not predicting well on minority class. I have also trained XGBoost classifier but that model also did not perform well.
From an interviewer's perspective, an imbalanced dataset is a common topic in interviews. Focusing on simple topics can increase your chances of success in cracking the interview.
I had to reupload this video because I forgot to include the part on ensemble techniques due to an editing error in the previous upload. Check timestamps.
Sir, please make a video on the difference between encoding and embedding
Sir please make a video on AB testing
I am writing to request your assistance in creating videos that delve into metaheuristic approaches, such as genetic algorithms, ant colony optimization, and others. It has come to my attention that there is a noticeable scarcity of resources covering these topics on platforms like TH-cam.
Sir make video about AB testing
Thank you sir for the best series on TH-cam, I just completed it in 2 months by watching 4 hr daily at 1.5x speed
Awesome explaining!!!
I'm not just talking about one video-the entire ML playlist is AWESOME! After consistently dedicating around 5 months to this playlist, I now feel confident in my ML skills. A big thanks to @Nitish Singh for this incredible journey!
Hi Sir as i have finished you 100 days ml playlist what can be our next step, please guide,
Thank You Sir for your great playlist and Great Explanation.
Thanks sir for this beautiful playlist. Never have i ever thought i would be able to understand all concepts of ml so easily. i am really grateful to you. i just completed this playlist after grinding for around 2 months. I have been going with some problems in my life that i am fighting but i will come out stronger now that I have completed this, soon I will start the DL playlist too and complete it too.
Thank you for everthing sir.
I am Shumail Malik, a PhD student at Hanyang University, South Korea. I have seen you transition from a young person to an older one over these 100 days of Machine Learning. Without a doubt, you have done great work for the public! Your teaching style and accent are truly impressive, and your simplicity in personality is very attractive. Thank you very much, Sir.
Hey what's your researching in ? How is life there?
Thanks sir for the best Ml playlists on TH-cam . Coming from a non techn background you make it really easy to understand all things
THANK you so much Nitish 😊u are the best in everything.🎉 Thanks for being my teacher 😊🙏
Another fantastic video by Nitish! Wonderful!!!
I understood everything sir
Thank you so much
You are the best
I am writing to request your assistance in creating videos that delve into metaheuristic approaches, such as genetic algorithms, ant colony optimization, and others. It has come to my attention that there is a noticeable scarcity of resources covering these topics on platforms like TH-cam.
Finally i completed this playlist🤩🤩
Sir pls do a session on cross validation.... There's no sperate video on cross validation in the ml playlist
Thankyou so much for this video, very helpful sir 🤌
Thank You Sir.
Sir, I truly admire your work and love all of your videos, learning so much from them. Thank you!!!
I have one question: at the end of the video you said that in spam filtering false positive is the critical one but if one msg is spam and classified as not spam(false negative) that will be the critical case isn't it? false negatives are generally considered to be more dangerous in this case because they can expose the recipient to potential harm.
I think false positive is more critical because it may send your important mail in spam which is more harmful rather than showing some spam mails as important mail.
please continue your llm transformers series.and also please upload nlp ner and topic modeling
i had this in an interview recently and i fkd up a bit :(
They gave me a dataset and were expecting how to handle such type of data and make it soo that the predictions that we make are reliable
Nitish :- At 7:00 It will be "Testing data" for determining the accuracy. Am I correct ?
Please make a new video on transformers 🙏
Awesome Content
Sir please do some working on MLOps playlist
Nitishi Sir please update your Machine Learning Roadmap and add links of your new videos (We want more and more videos of yours)
Hello sir please make video on ..PCA, tSNE, UMAP.
Dear Nitish sir, plz make video on how to fine tune our custom data using LLama llm.
Sir, when will you start a new batch for DSMP?
Sir Also make video on multi label classification problem.
Hi Sir,
Big Fan!!
I was searching for class imbalance video and you have uploaded it on right time.
I am training an ANN model for customer churn prediction where my dataset has class imbalance issues 96:4. I have used Upsampling, Downsampling, SMOTE, SMOTE-ENN, Class Weight but neither of them gave promising results and fail to predict well on minority class the recall value is very low. What should be done in such case where the model is not predicting well on minority class. I have also trained XGBoost classifier but that model also did not perform well.
very helpful video
Thank you very much sir
my test dataset is imbalanced . can i apply oversampling on test dataset???
Thanks
Thanks Sir
Is this series complete or anything remaining sirm
Hello sir, how can i connect with you ? Need urgent help please
42:46
it's better
🖤
Playlist ke end tak aate aate aisa lag rha ki aap jawan se budhe ho gye.
Sab LLM ki bat Kar Rahe hai app Machine learning par ruke hai
Bhai LLM ke bhi videos cover kar Rahe hai nitesh sir. To us, these concepts are still gold and they are used everywhere.
@@abhinavkale4632 bhai sir ke sare video mere laptop me hai all total video LLM ka history padhe hai abhi tak
From an interviewer's perspective, an imbalanced dataset is a common topic in interviews. Focusing on simple topics can increase your chances of success in cracking the interview.
ML hogya kya apka
@@samarmohanty6109 ha Mera generative Ai bhi ho gaya
Nitesh bhai aapka knowledge perfect hai but video itne long hote h ki chahke bhi pura nahi dekh pate.. please try to make video in short way🙏🤝👍
Sir app bahut peeche hai