Hello guys, so thank you for considering this amazing course.
Uh, in this module we are going to discuss about which all Ides we can specifically use to execute
our Python code.
Now, uh, this entire course has been recorded in Visual Studio Code.
Uh, so that installation, I will also show you, uh, we are also going to use Anaconda over here,
but they are still multiple options of using different, different Ides.
Uh, because many of you may have some of the concerns with respect to your laptops or with your desktop.
So the first ID like let's say that if you do not have a powerful system, I would suggest go ahead
and use Google Colab.
Now, Google Colab is an amazing IDE which has been provided by Google itself and it is completely for
free.
You will get sufficient amount of Ram and hard disk so that you can execute the code very much easily.
Okay, so first of all, just go and search for Google Colab over here in Google and you will be able
to get the first link over here.
So once you go over here, once you click over here.
So it will first of all go to your uh this specific link okay.
And in this link you can see that make the most of your collapse.
Description.
This is some of the information.
Let's say you want to go ahead and start a new notebook in drive.
Just go ahead and click on New Notebook.
So as soon as you probably click on new notebook over here.
So a new Google Colab environment will be created for you entirely.
And you can go ahead and execute your code over here.
Okay.
The best thing will be that this entire thing is running in the Google Cloud itself, right.
So you don't have to worry about if you have specific constraint with your local system.
And.
All right.
So once you go over here you can see right.
And over here if you click on the folders you can also upload the data set if you really want.
But before going ahead and showing this, what you really need to do is that you need to click on connect.
And the best thing about this is that you will also be able to get GPUs and all.
I'm using Google Colab Pro version, but for the normal Google Colab version.
Also, I think you will be able to get a100 GPU and all.
Okay, so you can use and obviously for your entire code execution, you don't require any GPU itself
unless and until you are learning about multithreading and all I think in multithreading also will not
require so much CPU.
You can probably go ahead and execute it.
Now here you can see that your kernel has been connected.
So over here somewhere around 12.6 GB of Ram is provided.
Uh, in my Google Colab Pro version and here you can see that hard disk is also somewhere around 225.
But for the free version also, I think you will be getting somewhere around uh, 8 to 10 GB of Ram
and hard disk.
Also, you'll be able to get somewhere around 75 to 80.
Within 100 GB I guess, which will be more than sufficient to execute the code that is available in
this entire course itself.
Right.
And here you can probably go ahead and write your code, your code itself.
If I'm probably writing one plus one, I will be able to get the answer.
If I'm writing Python code, let's say two plus three, I should be able to get the answer over here.
Let's say I want to go ahead and create list okay.
And if you don't know about list, don't worry, I will be teaching you in this entire course.
So but just to show you one example, if I want to go ahead and create a list, I will be able to create
it.
And if I want to print this list, I should be able to execute it over here.
Right.
So executing this entire cell.
So this is something called a cell right.
If you really want to.
Execute it.
You can either click on this run button or you can also go ahead and click on shift enter.
Okay.
So that is the shortcut to run any cell itself okay.
And over here you'll be able to see that you have something called as code and text cell.
In the code cell you can specifically go ahead and write any code that you really want with respect
to.
Python programming language.
Let's say you are inside this particular cell.
After this cell I want to.
Create a text cell.
I can also go ahead and create a text cell.
Uh, let's say that I will be using a bold sign over here something like this.
So this will be looking like a bold text.
This is Python demo okay.
And again, to execute it I will just go ahead and right shift enter.
So this is Python demo is basically getting displayed over here.
So this is the entire code over here.
And you can write down the entire code.
Whatever things we'll be showing in Python you can just go ahead and execute in the similar way okay.
now there are some more very important things that needs to be shown.
So over here you'll be able to see one folder location, right?
Uh, in the folder location there will be something like, uh, file upload over here.
Right.
So what you can do is that you can click on this upload.
Let's say you want to upload any data set.
You want to upload any, any ipynb file like any file that is created in Jupyter notebook.
Right.
So this is just like a Jupyter notebook itself.
So this will basically be giving you a ipynb file format.
Right.
So anything that is saved you can go ahead and rename it.
You can save it in that way.
And everything will be getting saved in your Google Drive itself.
Okay.
You need to be connected to your Google.
So here you can see my Google account is over here and it is connected over here.
Right.
You can also upload your data set over here.
Any data set that you want to upload you can specifically upload over here.
Let's say that there is some example over here I have this California housing test CSV.
If I just go ahead and copy the path, I should be able to read this particular data set with the help
of pandas or any other library itself.
If you want to upload any data set of yours, you can also upload it over here.
Okay, the next thing over here is that you can also download this entire notebooks.
Or you can see there is an option something called as download.
You can go ahead and click on download as a dot ipy and B you can download like this.
Or you can also download in PDF format okay.
So any file that you are able to write it will also be getting automatically saved.
And you can also reuse it.
So this is one of the amazing ideas that are available even people for deep learning training models
also.
Right?
If you know about machine learning or deep learning, they are also using this to train their model
because it is sufficiently fast, you know, it provides some amazing resources and Google is doing
that fabulous job.
So if you have a concern with respect to your system and all, you can directly use this.
Let's say that you're not able to install VS code, which I'm also going to show you as we go ahead.
If you face any kind of environment issues or anything, if you really want to just practice the DSA
part, Python part and all, you can just go directly into the Google Colab and you can start doing
it.
Okay, so this is the first IDE.
In the next IDE, I will be talking about GitHub code space.
Right?
So yes, this was it from my side in this video.
I will see you in the next video.
Thank you.