Using AI To Train AI

hello all my name is krishak and welcome to my YouTube channel so guys today in this particular video I'll be showing you how you can use AI to train Ai and in this video I'm going to talk about this amazing platform which is called as Julius we'll try to see what all capabilities Julius basically have why we specifically say this as an AI data analyst or a data scientist so please go ahead and watch this video till the end because it will be super beneficial for all the data scientists and data analyst out there now first of all to start with what you need to do is that go ahead and log in right and you can probably log in with your email ID whatever email ID you have like Google gith GitHub or any other things now here what I'm actually going to do here as soon as it it gives you an option okay so first of all I'll talk about this AI setting option so in the AI setting you can probably select the kind of AI that you want so it has three options over here open AI GPT 4 anthrop anthropic cloud cloud a Mr 7B and here you can also select elect a Persona right whether you probably want in science economics sales Healthcare marketing biology right so that this particular entire chart bot works as a kind of assistant now what I'm actually going to do over here by default I'll not select anything because I really want to show you how this AI can probably train another machine learning models and all so over here what I've done is that I've taken one uh data set okay and this data set is basically called as housing. CSV or I I'll be probably taking this particular dat data set so this is an advanced house price prediction data set because in this data set you specifically have lot of Nan values and all now see as soon as I probably uploaded it went ahead and it probably started please go ahead and load the files it gave this default conversation Vis visualize something to help present it okay so some default you know prompt is already given over here and here you can probably see it started writing the code and with respect to this particular code you'll be able to see that we'll be getting some kind of visualization we'll getting some kind of things right I'll be showing you there are much more different different options and finally we'll also try to train our model here you're not even writing a single line of code if you know all these techniques if you know the life cycle of a data science project I think this will be definitely helpful now here you could see that as soon as we went and told that okay visualize something to help present it here is this specific data set this data set has a lot of Nan values guys different different features and there are so many different features and how quickly you can see that the data set has been successfully loaded and the head of the data set is displayed above and there's some visualization okay now you can ask anything you can probably ask what is the correlation with respect to all the numerical features all the kind of prompts that you specifically want to ask okay so let's say I will go ahead and say perform all the necessary feature engineering for training a model training a regression model considering what will be my output feature over here so I'll just go ahead and select the sales price see already it is giving you code so if you click probably over here and show code you'll be able to see this entire code over here right so this is why this is specifically very amazing okay now what I will do over here I I've just given this particular prompt Pro perform all the necessary feature engineering uh for uh training a regression model considering your sales price as output feature okay sales price as output feature okay so I've done this so over here now as soon as I press enter you'll be able to see that automatically this AI by just seeing the feature it will be able to perform all the necessary feature engineering which is quite good guys trust me as a human being just to see this specific feature because see uh this end to endend project I've already created in my YouTube channel regarding Advanced highest price prediction and just the feature engineering videos from for somewhere around 30 to 35 minutes just imagine automatically it is being able to create the pipeline column Transformer whatever Transformer is required here model is also created he have Dro sales price sales price is in the dependent feature so here you can see all the necessary code is already given over here right and just imagine just to perform all these things we had to in initialize we have to probably see about all the features see the properties of the features and probably go ahead and try to see what kind of output we are basically getting right so they were a lot of statistical analysis but just but with the help of this Julius AI platform you'll be able to see that how quickly we are able to do this and later on train a model right so this is what is so amazing about this right and probably now if I probably see see the manual part that I had actually done this particular project end to end project uh there the video that was recorded was somewhere around 3 to 4 hours right and the amount of efforts was somewhere around 5 hours let's say okay but just by seeing the code I can feel that okay whatever things I have actually done over there is similar matching to all the things that this particular AI B has actually done right so this is quite amazing right and here is what is your response you can probably see what is the output that we are specifically getting uh because it has internally done all the test and uh train and test div divide of the data set that we had now along with this we can also ask for suggest prompts okay suggest some prompts other prompts to try right so over here it will also provide you some suggestion right here are some suggestion you might consider right model evaluation evaluate the performance of the regression model so let's go ahead uh let's see with respect to feuture importance okay so here I will say identify the most important feature that influences this right and I will stop this because I don't want further because I got the question and you can ask any question that you want you can probably ask how is the correlation of all the independent features right now feature importance actually helps you to select the most important feature in the data set now automatically the code is being written over which is quite amazing guys I have never used permutation importance but I can also tell it to probably use random Forest importance KNN importance all the things I can also tell go ahead and find if there is any kind of outliers and all and all right so this is amazing so see automatically the code has been written with respect to this I can probably check out this particular code and even execute in my jupyter notebook right really impressed with this specific tool guys I think this is far much better than the code interpreter that we have in chat GPT right so here you can probably see this overall call uh this this particular feature this particular feature in in the form of descending to ascending order right the visualization the listed outputs represent the top 10 most important feature influencing house prices according to the model right now this is one of the thing right now now what I can also do is that see if I probably go ahead and click on visualize so this is already the options that are specifically given over here so here it says give me another visualization to understand the data so I can probably also do that right uh I will just stop this what I'll do give me another visualization to understand for uh to further understand the data and uh give it uh and uh provide an Excel file so I want that particular visualization to see in an Excel file let's see whether it gives or not right so all the visualization it probably does and it provides that particular entire visualization in the Excel file so that tomorrow if you're working in the specific company you can also provide this entire information to the stakeholder again super amazing the features that you specifically have now see this is what is the correlation it is probably doing you know and it has probably also created this xlsx and now it will also give you the link right so here you have this specific link the image URL is there you can probably use this API all this visualization will be put up in an Excel sheet and here from here you can probably divide uh download it and you can use it now see the Excel file containing the correlation Matrix has been saved and can be downloaded from here again another important information see at the end of the day guys this is really saving your time if you are that early person who have already started machine learning deep learning feature engineering you have probably created multiple projects trust me just by using this it becomes so easy so beautiful and all right so this is how things are done I'll show you one more feature you know so probably uh what I had actually done is that just uh some some few few hours back you know I was checking out one of one another data set okay and that data set also had a huge amount of data over here what we specifically did is that it was trying to whenever it used to get an error right it used to probably Backtrack on that error and try to solve so if it is getting a specific error over here it is not telling me to provide any inputs it is backtracking and it is trying to solve that particular error right so again it soled that error then again it got an error what it did again it went and understood the root cause again it made the code right and again started giving the code over here right and finally it was also able to train the model right so let's let's go ahead and try to see some more features uh I will also uh like to see the evaluate the performance okay so I will go ahead and execute this code okay and suppose if you don't have anything in your mind you can use those three options visualization keep going or suggest any kind of prompts you know automatically it'll be taken care of right now here you can probably see the mean absolute error mean squared error everything is probably performed right uh I'll also tell it to perform hyperparameter tuning now the next thing will be that right and again the code is is written guys super amazing super super amazing this is just to show you how easily how your work is basically getting reduced okay also I will say also perform hyper parameter tuning you can take up any data set right because we whenever we initially get a data set if you just thinking about that particular data what it says you know it takes time right if you probably use this so here you can see gritzer CV this this this is there and yes it is probably using decision tree regressor right and done this is good great so your output is basically coming mean square error is basically there it is printing the things it is giving you the answers and all right and automatically the code see see the speed I like I really like the speed over here you know the backtracking part whenever it gets gets an error U whenever it is probably whenever I give a specific prompt you know and how difficult that particular problem may be right it gives you the kind of answer that is basically required at the end of the day it uses those three model but additionally on top of that UI part which has so many different things that is probably coming in front of you so uh go ahead and try it out uh Julius AI again guys uh use it for your productivity okay uh it'll be always good if you are able to complete many of the task easily and quickly as I say that couple of years back when chat GPT was not there when this all AI features were not there you know probably we used to take a lot of time to implement all the projects but now opportunities has been given to you all you have to be productive all you have to probably use this kind of AI tools to make to probably showcase or complete the project in a quicker way so here you can probably see all the output response everything I'm probably getting over here go ahead try it out and again uh amazing tool altogether with respect to Julius a quick shout out I think people should use it and probably use it to make thems productive so yes this was it for my side I hope you like this particular video I'll be seeing you on in the next video have a great day thank you one all take care bye-bye

Channel: Krish Naik

Views: 17,519

Keywords: yt:cc=on, how can AI train AI, Julius AI data nalyst, Julius AI data scientist, can we train AI models, machine learning tutorials, deep elarning tutorials, krish naik ai tutorials

Id: HWbbQChO_u4

Length: 12min 10sec (730 seconds)

Published: Fri Dec 08 2023