Welcome back to my eighth class of Microsoft Azure Machine Learning Studio. And in this this is our final class actually, probably we can go and you know, we can move. Keep working on this one. But you know, it's so homework, you can try this. Okay? experiment, we are done with the experiments.

Let's try to see this notebook. Okay. I have not used this you can either use from the local file, it has to be a Python. And this is a blank. So you have to have some code. I don't have any code.

So let's review this can be let's see, what is this all about? Yeah, this has to be a bit complex. Let's try to see this. different chords so it's so this notebook is actually good if they the chord probably is should be a chord actually it's a good riff you have a let's I okay this already open, good. still working on it, okay. Okay so this is the Lord Berg where you have this one um so this is actually the code which is there on this okay?

So this is not a no, this is somebody on so actually I was looking for okay let's let's copy the new let's say if I have Python c Python three and I will open that and I will be using let's see if I want to use this Let's say this is a by Tom right? This is from Python to Python two. So this might not work. Okay, let's use deleted. So what I do here is go here and go to Python two. Okay?

And create a notebook. Here we are here and we actually run the code actually. So what we'll do here is first in line, one match Try to use this one and let's run this important thing is disposed to so this is this will take online Python but it's not working, this information is actually not working so. So what we got from data fighter 635 is it ran but it gives some error here. This error has nothing to do with it. So way about it.

So let's use this one the second code. So this is Python script. Okay, we are running this Python. We let's not worry about it unless we get reserved. We are not getting resolved for you regard. So let's don't worry about this error at this point of time.

This is so the whole point of this is actually to to run the Python script without using Python. So we got this on surf right? And we've got this fine seven 4.406. And then so it's working well. So nothing to worry about those errors. Of course, there are errors.

Probably I'm being something wrong or probably that's not good. Number two is this looks good. This is running good. So there's no is for running your Python script, Nando's Python, you know, you can use our script if you if this block it opens a blank ask it Ah, there's a lot of other things can use it. Another one in show you so this is working right, it's working good. Let's use another another script.

So just use the script and use this one and manage this time no errors this finger so that's good. Um so these these are four. Let's use this embed services This is completely different So this is install Python, you use this online. So that's good. That's good. Everything is looks good.

So you know, you can use this one. Okay, this angle we're trying so so this is what the use of the notebook, okay? We use Python using in this machine learning. Okay, that's good to know. Okay, we saved this This was good. Let's use some of the example here and try to see if that thing works.

So I'm not actually going deep into the script because my intention was not to work on Python scripts. But we are just trying to Oh, this is just an example actually. So this was just an example where we actually did. So that's pretty much about my book. I mean, this Nope. Book is more for using your scripts Python scripts and our scripts.

If I even if I open this it will just open the tutorial as this is opening it will the transcript if you want to have to use a new script and use this one and run this, okay, that's all you can do. Okay, so this is our script for you want to open in no book niches are blank book and go to does it and there we will run this let's see how this works. This is basically language learning. So let's do this and grind this. It has given us more than one point time flow rate to the difference in values are interesting. Okay.

Let's see. So this is quoting active, no more to good schooling. My intention was not to do with anything in the coding but this gives any value no okay suppose to give this summary of the summary longer than this Yes. So, the whole point of this notebook is to run the script. So scripts are already there, use the scripts which is there here and try to run this but my whole point of this was the experiments So, we do we run this you know with the experiments and see this how it's working out? Okay, so that's pretty much for I think I completed most of this.

Okay, we have completed the administrator we have ran this various experiments. We have ran for we have done For radius for income prediction income prediction for predictive experiment that for web services from should apply it in web services you get in different predictive model. And then we did for automobile price prediction. And then once you devise your services to get prediction experiment, and then we did for data processing and analysis, which we added a new model, I'm sorry, which we added a new evaluation monitor and evaluate it and view the graph. And that was pretty interesting. And we deployed this web services.

So prediction model got generated for this. And then the last one we did is cross validation for regression. Not last but least then we did for goop, Iris data and auto generated the web services for this predictive model. And then we use this notebook For our experiment for Python and Allah, so pretty much we have covered everything as a tutorial point of view. So maybe not. I didn't touch any code.

Sorry about that. Not that I didn't want it to. My intention was not to do anything. But since I wanted to explain you about the notebook, that's the reason I wanted to go there. But otherwise, most of this was an experiment. And also we did for project two, we added some of these assets here.

And it should give access to other people and we can use it. And we were administered we are already I already explained that in the first class. It's still showing zero dB. So maybe we're using only notice, okay, so and I also am giving you the free data. If you don't like the data, what is their users healthcare data, this is for your homework. Are your practice you can use that.

Okay? So you can use that one for your own model creation, any model, take your stream or K means clustering or any linear regression or, you know, it's up to you. So what are the what are the random you want to use? So that's pretty much. So with that, I'll end this class. If you have so here's the thing, you go like this, I understand.

You may not like this. You can come up with questions which I missed it, either our youth can visit. This documentation, there's a documentation. Also ever handed this documentation for your information and go to go to Azure Machine Learning Studio. That's all we have. The bullet here you have probably all the governess experiments predictive experiments in samples how to get out so you can go to here you know you can use this so you can use a lot of things and you can create your own model Okay, you can create your own martyr vision this one and create any predictive value for the stream Be careful with shrinking string it will take only the string what is there so if it's let's say the make is fool or make is for you know Nissan or something, it all gets the string it doesn't get the you know the alphabets return looking for.

So be careful with that thing. Okay, you want to convert that string into ASCII value and then we do more work. prediction probably are probably going to do the prediction for that prediction is good for numeric values rather than the strings. So that's pretty much we know with that in this class, and I hope you liked this video. Now. If you don't, you can come back with, you know, with questions and answer any question to email, or you have this email.

So, you know, he talked about machine learning, so it's all about data. So if you know about data, you cannot upload anything everything will be done here. Actually, let me show you once again. Everything this thing that you want to use right, this train models transformations, mathematical statistical analysis, convert your converted data, conversions, data input and output. In the you have this data transformations. Of course, that's more importantly, where you can use any kind of, you know, transformation of data and the feature selection and machine learning.

That's why it's important. Where we have is anomaly detection, classification, clustering and regression, you're locked into regression and the score to get the monitor the score values, and then we go into training, how do you want to create the model three clustering and we didn't do all this, not that we cannot do it. But it's, it's you have to do it, you know, do this and publish paper that should be good for you. thing and then we went to mode book, where we actually try to do two things. One is by Honda, another is Oscar. So that covers pretty much the tutorial binder.

Yeah I don't say that we are in I would not say in expert level because an expert level you have to go each and everything in detail but, but then again, it's not meant for expert because you have to do your part two right if I haven't learned something, so I'm, you know, I'm a discus. Thanks for watching. No, I hope you like this video. Yeah and good luck