WEBVTT 00:00.200 --> 00:03.710 Hello guys, so thank you for considering this amazing course. 00:03.710 --> 00:10.820 Uh, in this module we are going to discuss about which all Ides we can specifically use to execute 00:10.820 --> 00:12.050 our Python code. 00:12.080 --> 00:16.760 Now, uh, this entire course has been recorded in Visual Studio Code. 00:16.790 --> 00:21.980 Uh, so that installation, I will also show you, uh, we are also going to use Anaconda over here, 00:21.980 --> 00:26.150 but they are still multiple options of using different, different Ides. 00:26.270 --> 00:31.790 Uh, because many of you may have some of the concerns with respect to your laptops or with your desktop. 00:31.790 --> 00:37.490 So the first ID like let's say that if you do not have a powerful system, I would suggest go ahead 00:37.490 --> 00:39.170 and use Google Colab. 00:39.170 --> 00:44.090 Now, Google Colab is an amazing IDE which has been provided by Google itself and it is completely for 00:44.090 --> 00:44.540 free. 00:44.570 --> 00:49.820 You will get sufficient amount of Ram and hard disk so that you can execute the code very much easily. 00:49.850 --> 00:55.670 Okay, so first of all, just go and search for Google Colab over here in Google and you will be able 00:55.670 --> 00:56.930 to get the first link over here. 00:56.930 --> 00:59.090 So once you go over here, once you click over here. 00:59.090 --> 01:02.870 So it will first of all go to your uh this specific link okay. 01:02.900 --> 01:06.380 And in this link you can see that make the most of your collapse. 01:06.410 --> 01:07.220 Description. 01:07.220 --> 01:08.900 This is some of the information. 01:08.900 --> 01:12.440 Let's say you want to go ahead and start a new notebook in drive. 01:12.470 --> 01:14.600 Just go ahead and click on New Notebook. 01:14.600 --> 01:17.660 So as soon as you probably click on new notebook over here. 01:17.660 --> 01:22.730 So a new Google Colab environment will be created for you entirely. 01:22.730 --> 01:25.130 And you can go ahead and execute your code over here. 01:25.160 --> 01:25.550 Okay. 01:25.580 --> 01:29.900 The best thing will be that this entire thing is running in the Google Cloud itself, right. 01:29.900 --> 01:34.490 So you don't have to worry about if you have specific constraint with your local system. 01:34.490 --> 01:34.640 And. 01:34.640 --> 01:35.240 All right. 01:35.240 --> 01:37.790 So once you go over here you can see right. 01:37.790 --> 01:42.830 And over here if you click on the folders you can also upload the data set if you really want. 01:42.830 --> 01:47.660 But before going ahead and showing this, what you really need to do is that you need to click on connect. 01:47.660 --> 01:51.830 And the best thing about this is that you will also be able to get GPUs and all. 01:51.830 --> 01:56.810 I'm using Google Colab Pro version, but for the normal Google Colab version. 01:56.810 --> 01:59.480 Also, I think you will be able to get a100 GPU and all. 01:59.510 --> 02:05.630 Okay, so you can use and obviously for your entire code execution, you don't require any GPU itself 02:05.660 --> 02:10.340 unless and until you are learning about multithreading and all I think in multithreading also will not 02:10.340 --> 02:12.050 require so much CPU. 02:12.080 --> 02:14.060 You can probably go ahead and execute it. 02:14.090 --> 02:18.080 Now here you can see that your kernel has been connected. 02:18.080 --> 02:23.840 So over here somewhere around 12.6 GB of Ram is provided. 02:23.960 --> 02:29.630 Uh, in my Google Colab Pro version and here you can see that hard disk is also somewhere around 225. 02:29.660 --> 02:35.540 But for the free version also, I think you will be getting somewhere around uh, 8 to 10 GB of Ram 02:35.540 --> 02:36.140 and hard disk. 02:36.140 --> 02:38.570 Also, you'll be able to get somewhere around 75 to 80. 02:38.600 --> 02:44.210 Within 100 GB I guess, which will be more than sufficient to execute the code that is available in 02:44.210 --> 02:45.350 this entire course itself. 02:45.380 --> 02:45.710 Right. 02:45.710 --> 02:49.550 And here you can probably go ahead and write your code, your code itself. 02:49.550 --> 02:52.790 If I'm probably writing one plus one, I will be able to get the answer. 02:52.790 --> 02:57.890 If I'm writing Python code, let's say two plus three, I should be able to get the answer over here. 02:57.890 --> 03:00.920 Let's say I want to go ahead and create list okay. 03:00.920 --> 03:05.360 And if you don't know about list, don't worry, I will be teaching you in this entire course. 03:05.360 --> 03:11.390 So but just to show you one example, if I want to go ahead and create a list, I will be able to create 03:11.390 --> 03:11.810 it. 03:11.810 --> 03:16.230 And if I want to print this list, I should be able to execute it over here. 03:16.260 --> 03:16.710 Right. 03:16.710 --> 03:19.800 So executing this entire cell. 03:19.800 --> 03:21.660 So this is something called a cell right. 03:21.660 --> 03:22.440 If you really want to. 03:22.470 --> 03:23.250 Execute it. 03:23.250 --> 03:28.650 You can either click on this run button or you can also go ahead and click on shift enter. 03:28.680 --> 03:28.920 Okay. 03:28.920 --> 03:32.880 So that is the shortcut to run any cell itself okay. 03:32.910 --> 03:37.770 And over here you'll be able to see that you have something called as code and text cell. 03:37.800 --> 03:42.600 In the code cell you can specifically go ahead and write any code that you really want with respect 03:42.600 --> 03:42.660 to. 03:42.690 --> 03:43.980 Python programming language. 03:43.980 --> 03:45.960 Let's say you are inside this particular cell. 03:45.960 --> 03:47.040 After this cell I want to. 03:47.070 --> 03:48.150 Create a text cell. 03:48.150 --> 03:50.400 I can also go ahead and create a text cell. 03:50.430 --> 03:54.420 Uh, let's say that I will be using a bold sign over here something like this. 03:54.420 --> 03:56.370 So this will be looking like a bold text. 03:56.370 --> 03:59.640 This is Python demo okay. 03:59.670 --> 04:03.060 And again, to execute it I will just go ahead and right shift enter. 04:03.060 --> 04:06.750 So this is Python demo is basically getting displayed over here. 04:07.260 --> 04:08.970 So this is the entire code over here. 04:08.970 --> 04:10.680 And you can write down the entire code. 04:10.680 --> 04:15.510 Whatever things we'll be showing in Python you can just go ahead and execute in the similar way okay. 04:15.590 --> 04:18.500 now there are some more very important things that needs to be shown. 04:18.500 --> 04:21.470 So over here you'll be able to see one folder location, right? 04:21.860 --> 04:26.420 Uh, in the folder location there will be something like, uh, file upload over here. 04:26.420 --> 04:26.600 Right. 04:26.630 --> 04:29.300 So what you can do is that you can click on this upload. 04:29.300 --> 04:31.100 Let's say you want to upload any data set. 04:31.100 --> 04:36.110 You want to upload any, any ipynb file like any file that is created in Jupyter notebook. 04:36.140 --> 04:36.260 Right. 04:36.290 --> 04:38.330 So this is just like a Jupyter notebook itself. 04:38.330 --> 04:41.600 So this will basically be giving you a ipynb file format. 04:41.600 --> 04:42.020 Right. 04:42.020 --> 04:44.630 So anything that is saved you can go ahead and rename it. 04:44.630 --> 04:46.010 You can save it in that way. 04:46.010 --> 04:48.740 And everything will be getting saved in your Google Drive itself. 04:48.770 --> 04:48.980 Okay. 04:49.010 --> 04:51.050 You need to be connected to your Google. 04:51.050 --> 04:55.070 So here you can see my Google account is over here and it is connected over here. 04:55.070 --> 04:55.370 Right. 04:55.370 --> 04:57.380 You can also upload your data set over here. 04:57.380 --> 05:00.530 Any data set that you want to upload you can specifically upload over here. 05:00.560 --> 05:04.880 Let's say that there is some example over here I have this California housing test CSV. 05:05.030 --> 05:09.710 If I just go ahead and copy the path, I should be able to read this particular data set with the help 05:09.710 --> 05:11.870 of pandas or any other library itself. 05:11.870 --> 05:15.830 If you want to upload any data set of yours, you can also upload it over here. 05:15.860 --> 05:22.010 Okay, the next thing over here is that you can also download this entire notebooks. 05:22.010 --> 05:25.430 Or you can see there is an option something called as download. 05:25.430 --> 05:30.080 You can go ahead and click on download as a dot ipy and B you can download like this. 05:30.080 --> 05:32.690 Or you can also download in PDF format okay. 05:32.690 --> 05:36.590 So any file that you are able to write it will also be getting automatically saved. 05:36.590 --> 05:37.880 And you can also reuse it. 05:37.880 --> 05:43.820 So this is one of the amazing ideas that are available even people for deep learning training models 05:43.820 --> 05:44.060 also. 05:44.090 --> 05:44.420 Right? 05:44.510 --> 05:48.980 If you know about machine learning or deep learning, they are also using this to train their model 05:49.010 --> 05:54.110 because it is sufficiently fast, you know, it provides some amazing resources and Google is doing 05:54.110 --> 05:55.070 that fabulous job. 05:55.070 --> 05:59.840 So if you have a concern with respect to your system and all, you can directly use this. 05:59.840 --> 06:04.160 Let's say that you're not able to install VS code, which I'm also going to show you as we go ahead. 06:04.160 --> 06:09.050 If you face any kind of environment issues or anything, if you really want to just practice the DSA 06:09.050 --> 06:13.910 part, Python part and all, you can just go directly into the Google Colab and you can start doing 06:13.910 --> 06:14.090 it. 06:14.120 --> 06:15.980 Okay, so this is the first IDE. 06:16.130 --> 06:19.610 In the next IDE, I will be talking about GitHub code space. 06:19.640 --> 06:20.030 Right? 06:20.030 --> 06:22.340 So yes, this was it from my side in this video. 06:22.340 --> 06:23.570 I will see you in the next video. 06:23.570 --> 06:23.990 Thank you.