Hi Mr siddhardhan, I hope you doing well. I am unable to access your Google drive dataset .If you don’t mind please forward dataset of sonar rock vs mine prediction to venkup900@gmail.com. Thank you,
sir in your previous video about types of supervised learning you said regression is used when it is about predicting quantity or continuous values like salary, age and price
This is Best ML Channel on UA-cam....Peoples don't wanna see Real Thing............They just see Appeling Claikbaits on UA-cam and Tries to Learn ML from those Guys..................
First of all this project is so interesting!!! You are amazing, teaching means telling me why exactly we are we doing what we are doing. Thank you for being a real tutor and not being boring too! i was always questioning my brains ability but your way of teaching made me realize that its not my brain its the way we are being taught. I bet your channel is going to bring me to the next level
I've watched many tutorials on just learning general concepts, and this was y far the most comprehensive, easiest to understand video I've watched. Thank you, you and these videos are amazing.
So nice dear a great explanation....Now I am your fan from Indonesia. I am the student of master in mining engineering and I find this video so interesting. I will apply this to one of my model soon. I have not enough words to thank you enough
I think you might have interchanged the accuracy score parameters in this video, but otherwise, this is really amazing!!!thank you for helping me scale up my ML skills. Could you please do more videos on computer vision?
Thanks, @Siddharthan bro! I had read about ML models before, but the concepts were pretty confusing. Now, I have a clear understanding of each operation-why we split into training and test sets, and how to measure the performance of the model. Thank you for the video! Keep up the great work!
The video is highly informative. Just a small issue I am facing, the volume of the sound is low. However, thank you so much for your effort. Great help, indeed.
What a wonderful video and the complete playlist as well. I was looking for something like this to improve my ML skills! One question - around 36:00, we see the model accuracy on training data to be about 83%... Shouldn't that be 100%? As we have created the model using the training data, and we are doing the prediction on the same data as well?
thanks a lot 😇 we won't get 100% accuracy all the time. we may need to do some model optimization to make better predictions. you can research about it.
@@Siddhardhan Thank's for your work, I'm wondering why should we compute the accuracy score on training data, I mean for what purpose ? I don't think it is usefulll
Hello!! Siddhardhan I have subscribed to your channel and accessing the videos on Machine Learning. The videos are very informative and precise. I would like to know about some certifications for Machine Learning, so that I can get a job or use it for my higher studies
Thanku so much Siddharthan for a wonderful machine learning project video. Your channel is really very good n videos r really great & one can get clearity about machine learning projects basics easily. Waiting for more such videos.....🥰
At 22:11 there is function which finds mean of Y variable " sonar_data.groupby(60).mean() " , How can mean of existing dataset which already have resut will be usefull to predict on dataseet which does not contain Y predict.
hi! it's not the result of the data. we are just exploring the data. we are just doing some data analysis. in this case, we are clearly seeing the difference in the mean value. but it's not the way the model understands the data. it tries to fit to the data and learn iteratively. we cannot create a model mentioning the mean value for all the columns. then it's explicitly telling the model about the data. and moreover, we cannot find this difference in mean in all datasets.
Hi Rakesh! Thanks for your appreciation!😇 I'll definitely make detailed videos on the theory behind important Machine Learning models. But I cannot do it in these project videos. I'll make a module separately in my machine learning course in this UA-cam channel, in which I'll explain about all the models in detail.
@@Siddhardhan Thanks.. Also want to know how we will approach if labelled data is not divided properly, In you example like R-111, M-97.. if those value not closed then how we will approach.. I am beginner so I realy like this demos. Thanks a lot
We can use methods like under-sampling and over-sampling. In under-sampling, we reduce the labels that are more abundant and choose the important data points that are unique. In over-sampling, we try to make new data points by analysing the data with low number of labels. We can use algorithms like Bootstrapping or Synthetic Minority Over-sampling for this purpose.
Sir thankyou so much for your machine learning course ,your teaching style is fantastic , I was really confused regarding the data sets and proper model working ,you really cleared my all doubts ,thank you so much sir
Thankyou for sharing your project. Due to your video I just now know how to apply my theory knowledge into practical approach. I would have loved it there were graphs also for logistic Regression understanding for beginners. Thankyou so much.
Thank you so much! I'll be posting project videos every Friday. Stay tuned! Monday and Wednesday will be basic videos for beginners. Thanks for your support!
I'll make them in the future definitely! Now the reshape part: While training the model, we use the dataset which has 200 examples (rows) with 60 features (columns). Now in the prediction part, we are trying to predict only one instance. If we don't reshape the array, the model will think that we are again feeding 200 examples & it will give a error message due to this. That's why we reshape the input array. You get it?
The idea is to test many classification algorithms Knearest neighbors, Random Forests etc, calculate the acuracy on each of these, get an average accuracy for each of these after some iterations or with cross_val_score and finally you pick the one with with highest average accuracy.
Interesting video with crystal clear explanation. Thanks!! I have a question, in this video we found accuracy of test data to be ~76%. Is there anything that can be done (fine-tuning) to the dataset to improve this accuracy??? Also, is there a way we can show the failure side (24%) of prediction??? Are there any other models that can be used to solve a binary classification problem apart from LogReg??? HELP WITH THESE QUESTIONS. GREAT VIDEO!!!!!
Hi Siddhardhan. Thank you for the video. You mentioned about the difference in the Means making an impact in terms of prediction. How did we use that in this video?
Nice explanation of the coding and the function calls. I'm disappointed that there's no real discussion on what the data is (beyond "R = rock, M = mine", much less why a certain model is suitable to fit it. These are things that I was looking forward to learn about, and things that seem to me to be rather key elements of any ML project. But I'm sure this video is helpful to someone who already knows such things.
hi! it's sufficient.. once you start working in different projects, you will get to know further concepts... the other topics I would suggest you would be class and objects...
Take it this way, a company wants to check using some features to know if a Rock or Mine is under their submarine, so they enter 60 features to check it. If it returns R or M, then we know what it is. So that input_data section is just a random example from the dataset to see if it's correct
Hello! Thank you so much for these videos! I've decided to watch 1 by 1 and will definitely enhance my skills in Machine Learning thanks to you! I have a small question which I'm slightly confused about. I watched your video on the types of supervised learning which are the classification like if an image is a cat or dog and regression. Since this project is predicting whether its a rock or mine, shouldn't it be a classification rather than a regression? Same case with the diabetes prediction project; shouldn't it also be classification because we want to know if someone has diabetes or not
When I said problem, i mentioned the problem statement that we are working on... For example, diabetes prediction.... Model represents the machine learning model that we are training.
Hello Siddhardhan. Very Good example and the way you explain it is really appreciable. Is it possible to further analyze data to understand which column has an effect to decide R or M.
Hi! You can join this Telegram group for regular updates about my videos: t.me/siddhardhan
Thank you!
Hi Mr siddhardhan, I hope you doing well. I am unable to access your Google drive dataset .If you don’t mind please forward dataset of sonar rock vs mine prediction to venkup900@gmail.com.
Thank you,
Dataset sent
already joined, nice channel.
@@Siddhardhan @srii The data source is missing column heading sir
sir in your previous video about types of supervised learning you said regression is used when it is about predicting quantity or continuous values like salary, age and price
This is Best ML Channel on UA-cam....Peoples don't wanna see Real Thing............They just see Appeling Claikbaits on UA-cam and Tries to Learn ML from those Guys..................
Hi! I'm a french student and I just found your youtube channel, your videos are really great and useful, thank you so much !!
Thanks a lot for your positive words 😇
Hey!! It would be great if you could share the documentation and code for this project. Please help your friend out.
@@lathaprasad1947 He has shared the google colab dataset and code in the description.
Camille.. i want to learn French.. please suggest me how to learn
@@santoshhonnungar5543 don't
First of all this project is so interesting!!! You are amazing, teaching means telling me why exactly we are we doing what we are doing. Thank you for being a real tutor and not being boring too! i was always questioning my brains ability but your way of teaching made me realize that its not my brain its the way we are being taught. I bet your channel is going to bring me to the next level
I've watched many tutorials on just learning general concepts, and this was y far the most comprehensive, easiest to understand video I've watched. Thank you, you and these videos are amazing.
your course is underrated and brilliant, god bless you sir!!!
Good Going @Siddhardhan you really are doing this so good. I legit found this the best channel for my study purpose
So nice dear a great explanation....Now I am your fan from Indonesia. I am the student of master in mining engineering and I find this video so interesting. I will apply this to one of my model soon. I have not enough words to thank you enough
Thank you so much 😇 I'll work constantly to add value to all the viewers of my channel 🤝
Assalamualaykum brother can you share your instagram or facebook i need to contact you ASAP
Excellent video...great work boss !!!
I think you might have interchanged the accuracy score parameters in this video, but otherwise, this is really amazing!!!thank you for helping me scale up my ML skills. Could you please do more videos on computer vision?
Thanks for this! Really great video, very intuitive even for beginner/intermediate python users.
😇😇
Thanks, @Siddharthan bro! I had read about ML models before, but the concepts were pretty confusing. Now, I have a clear understanding of each operation-why we split into training and test sets, and how to measure the performance of the model.
Thank you for the video! Keep up the great work!
This is some real good step by step explaination. Thanks for all your good work!
I just suscribed! Your content is really helpful to start doing projects which are so needed to develop skills on this field. Keep it up my friend.
The video is highly informative. Just a small issue I am facing, the volume of the sound is low. However, thank you so much for your effort. Great help, indeed.
thanks sir for this work. you are blessed. you have really contributed something huge to your generation. we appreciate it.
Amazing
Your videos terrific and very helpful. Your explanation is very clear and understandable. Thank you so much
Very good tutorial. Thanks!
Your presentation is priceless...just incredible! keep doing such wonderful tutorials.
Thanks a ton!😇
great tutorial, love from india
*We need more such videos related to projects in MACHINE LEARNING*
Bro, your Work is fantastic and loving it. Very Much Appreciated
What a wonderful video and the complete playlist as well. I was looking for something like this to improve my ML skills!
One question - around 36:00, we see the model accuracy on training data to be about 83%... Shouldn't that be 100%? As we have created the model using the training data, and we are doing the prediction on the same data as well?
thanks a lot 😇 we won't get 100% accuracy all the time. we may need to do some model optimization to make better predictions. you can research about it.
@@Siddhardhan Thank's for your work, I'm wondering why should we compute the accuracy score on training data, I mean for what purpose ? I don't think it is usefulll
@@mohamedhamiche yeah same.
I am looking for this type of videos in which full and clear explanations on Machine learnings.. Thanks a lot
You are welcome
Just completed the implementation. Great video, thankyou.
🎉
Hello!! Siddhardhan
I have subscribed to your channel and accessing the videos on Machine Learning. The videos are very informative and precise.
I would like to know about some certifications for Machine Learning, so that I can get a job or use it for my higher studies
finished practicing coding.Feeling a lot more confident
You have a really good channel!
You are an amazing instructor sir!
This video is so easy to understand!!
Real life working and advantage of this project....
thank u sir it was soo helpfull and easy to understand
Organized Delivery.Excellent sir!
Thanks a lot 😇
Thanku so much Siddharthan for a wonderful machine learning project video. Your channel is really very good n videos r really great & one can get clearity about machine learning projects basics easily. Waiting for more such videos.....🥰
Wow... never thought about such a project. Super Thambi.
Sir, Which is better Anaconda Jupyter notebook or google colaboratory
Such a great teacher 🔥
Well explained. Great Job!👏👏👏
At 22:11 there is function which finds mean of Y variable " sonar_data.groupby(60).mean() " ,
How can mean of existing dataset which already have resut will be usefull to predict on dataseet which does not contain Y predict.
hi! it's not the result of the data. we are just exploring the data. we are just doing some data analysis. in this case, we are clearly seeing the difference in the mean value. but it's not the way the model understands the data. it tries to fit to the data and learn iteratively. we cannot create a model mentioning the mean value for all the columns. then it's explicitly telling the model about the data. and moreover, we cannot find this difference in mean in all datasets.
Thank you so much ! wonderful lecture..
Your explanations are very clear. Can you please do some tutorials on probability pls? I have big issue in that
This is soooooo good; Blessings buddy.
very good.. Request you give more theoretical concept on each algorithm topic, it will be easier for us then to understand the usecase and practical
Hi Rakesh! Thanks for your appreciation!😇 I'll definitely make detailed videos on the theory behind important Machine Learning models. But I cannot do it in these project videos. I'll make a module separately in my machine learning course in this UA-cam channel, in which I'll explain about all the models in detail.
@@Siddhardhan Thanks.. Also want to know how we will approach if labelled data is not divided properly, In you example like R-111, M-97.. if those value not closed then how we will approach.. I am beginner so I realy like this demos. Thanks a lot
We can use methods like under-sampling and over-sampling. In under-sampling, we reduce the labels that are more abundant and choose the important data points that are unique. In over-sampling, we try to make new data points by analysing the data with low number of labels. We can use algorithms like Bootstrapping or Synthetic Minority Over-sampling for this purpose.
I hope this clears your doubt.
@@Siddhardhan Thanks , yes theoratically it is cleared, but want one short demo on over samplimg and under sampling when you will get time.. 😀
Sir thankyou so much for your machine learning course ,your teaching style is fantastic , I was really confused regarding the data sets and proper model working ,you really cleared my all doubts ,thank you so much sir
You are most welcome😇
Very nice tutorial , u r helping me alot thank you so much 🎉
Thank you wish you all the best👌
good lecture, i like this and i understood very well
Great explanation. Looking forward to learn as much as possible.
Glad it was helpful!😇
Loved the way explained.
thank you so much bro for
such a awesome video , keep it up .....
Most welcome 😊
superb bro, you are great instructor, i had never seen such explanatory video.
Thanks a ton😇
so well explained brother i was so confused with all these libraries & funcs...
thanku so much
Thankyou for sharing your project. Due to your video I just now know how to apply my theory knowledge into practical approach. I would have loved it there were graphs also for logistic Regression understanding for beginners. Thankyou so much.
Excellent ..thanku so much bro
Found very useful, thanks and keep up the work
Excellent!!want more intermediate to advanced use cases.
Thank you so much! I'll be posting project videos every Friday. Stay tuned! Monday and Wednesday will be basic videos for beginners. Thanks for your support!
Thank you very much sir
Amazing video. Loved it❤
I wish I can catch what you are talking about, you have a very unique accent...
tq for the project
can you explain 41:50 reshape part again?
will you also upload data science projects?
I'll make them in the future definitely!
Now the reshape part:
While training the model, we use the dataset which has 200 examples (rows) with 60 features (columns).
Now in the prediction part, we are trying to predict only one instance. If we don't reshape the array, the model will think that we are again feeding 200 examples & it will give a error message due to this. That's why we reshape the input array. You get it?
Thank you very much. Very useful tutorial
thanks sir for this work. you are blessed
Thank you so much sir. You are so helpful!
Could you please expand why was Logistics Regression used instead of any classification algorithms?
The idea is to test many classification algorithms Knearest neighbors, Random Forests etc, calculate the acuracy on each of these, get an average accuracy for each of these after some iterations or with cross_val_score and finally you pick the one with with highest average accuracy.
Interesting video with crystal clear explanation. Thanks!! I have a question, in this video we found accuracy of test data to be ~76%. Is there anything that can be done (fine-tuning) to the dataset to improve this accuracy??? Also, is there a way we can show the failure side (24%) of prediction??? Are there any other models that can be used to solve a binary classification problem apart from LogReg??? HELP WITH THESE QUESTIONS. GREAT VIDEO!!!!!
i think we need more data for more accuracy
thank you very much , good job
Good explanation bro🔥🔥
Thanks brother!
Hi Siddhardhan. Thank you for the video. You mentioned about the difference in the Means making an impact in terms of prediction. How did we use that in this video?
Nice explanation big thanku to you pls make more and more videos related to data science project and provide some guidance...
23:00
Very useful video bro🔥
Thanks bro😇
Great video to learn how to ML on Data Sets. I am struggling in the EDA and Data Preprocessing part!
Really cool content !!! 😁👍
thanks a lot 😇
Really helpful for my projects.
you're welcome
Thanks fot the exercise, I love it.
Nice explanation of the coding and the function calls. I'm disappointed that there's no real discussion on what the data is (beyond "R = rock, M = mine", much less why a certain model is suitable to fit it. These are things that I was looking forward to learn about, and things that seem to me to be rather key elements of any ML project. But I'm sure this video is helpful to someone who already knows such things.
here the target variable is binary class (2 values only).Such cases can be fitted by logistic regression model
Thnku sir❤
simply superb bro awesome.
Is python course in your channel is sufficient for machine learning?
hi! it's sufficient.. once you start working in different projects, you will get to know further concepts... the other topics I would suggest you would be class and objects...
thanks for your positive words 😇
Useful playlist, thank you!
you're most welcome 😇
Hi u have selected input_data why did u do that step and are the inputs taken random or particular
Take it this way, a company wants to check using some features to know if a Rock or Mine is under their submarine, so they enter 60 features to check it. If it returns R or M, then we know what it is. So that input_data section is just a random example from the dataset to see if it's correct
Hello! Thank you so much for these videos! I've decided to watch 1 by 1 and will definitely enhance my skills in Machine Learning thanks to you! I have a small question which I'm slightly confused about. I watched your video on the types of supervised learning which are the classification like if an image is a cat or dog and regression. Since this project is predicting whether its a rock or mine, shouldn't it be a classification rather than a regression? Same case with the diabetes prediction project; shouldn't it also be classification because we want to know if someone has diabetes or not
Hi! yes. These are classification problems only. Logistic regression is actually a classification model.
Siddhardhan whats the difference between a class model and class problem
When I said problem, i mentioned the problem statement that we are working on... For example, diabetes prediction.... Model represents the machine learning model that we are training.
good concept, need some basic projects which beginners can add-in resume
This is a very basic one... Try to learn this and execute the code by yourself. Will add more videos every Friday. Thanks!
thank you very much bro
Wow! Nice video! And also learnt about Google Collab which was great. Thanks! Would love to connect with you
That was a great tutorial
Thanks a lot for this Amazing video
Most welcome 😊
How you came up with the decision of using logistic regression?
just a random decision we can use others too, in my case i used random forest and it had better result but might have some overfitting😅😅😭
Thanks a lot !
Thanks a lot, Sir
do i need to know pytorch or tensorflow to do these projects
Good explanation bro..
Thank you so much!😇
Thank you, Sir.
You are very welcome
WOW NICE BUT BRUH LITTLE SHORTER VIDEOS PLAYLIST PLEASE
Hello Siddhardhan.
Very Good example and the way you explain it is really appreciable. Is it possible to further analyze data to understand which column has an effect to decide R or M.
thanks a lot 😇 yes, you can do some data analysis on the data
Excellent 😍😍
Thanks 🤗
Thank you so much sir..you explain very clearly..it helped a lot.😁
Most welcome 😊
I am Taking Challenge That 1 project Per Day of this playlist #39 videos #39 days
i increased the test size to 20% which got me accuracy of 81%. and accuracy of training data is still 83%.