1 00:00:00,100 --> 00:00:01,966 All right, let's keep the suspense. 2 00:00:01,966 --> 00:00:06,233 And now, whenever you're ready, let's create a copy of this file. 3 00:00:06,266 --> 00:00:08,566 Because this one is in read only mode. 4 00:00:08,566 --> 00:00:11,800 And therefore we want to create a copy in order 5 00:00:11,966 --> 00:00:15,000 to re-implement it from scratch together. 6 00:00:15,400 --> 00:00:16,900 And make sure to focus. 7 00:00:16,900 --> 00:00:19,900 Because, you know, this will be quite more advanced than before. 8 00:00:20,100 --> 00:00:22,433 So let's put that right here. 9 00:00:22,433 --> 00:00:23,100 And there we go. 10 00:00:23,100 --> 00:00:25,300 Now let's remove the cells. 11 00:00:25,300 --> 00:00:27,866 And let's try not to look at the final result. 12 00:00:27,866 --> 00:00:28,566 Right. 13 00:00:28,566 --> 00:00:30,533 You just put your eyes around here. 14 00:00:30,533 --> 00:00:31,966 You don't look at the results. 15 00:00:31,966 --> 00:00:35,933 Because let's try to keep the surprise of the predicted salary up to the end, 16 00:00:36,133 --> 00:00:39,133 up to the final execution of the code. 17 00:00:39,266 --> 00:00:42,666 All right, so let's remove all this and this as well. 18 00:00:43,100 --> 00:00:45,266 This is well, only the code cells. Right. 19 00:00:45,266 --> 00:00:48,633 Please keep all the text cells so that we can keep 20 00:00:49,133 --> 00:00:52,800 the well highlighted structure of this implementation. 21 00:00:53,600 --> 00:00:54,566 All right. 22 00:00:54,566 --> 00:00:59,433 And now really make sure to not look at the result. 23 00:00:59,433 --> 00:01:03,200 You know the output of the code cell because that's where you will see 24 00:01:03,200 --> 00:01:04,400 you know, the final results. 25 00:01:04,400 --> 00:01:04,766 All right. 26 00:01:04,766 --> 00:01:06,233 I managed to do it. 27 00:01:06,233 --> 00:01:09,000 I did not look at it. Even if of course I know the result. 28 00:01:09,000 --> 00:01:12,000 But what I mean is that it was totally possible not to look at it. 29 00:01:12,366 --> 00:01:14,233 All right, so that's the whole structure. 30 00:01:14,233 --> 00:01:17,366 And now we're ready to start this implementation. 31 00:01:17,766 --> 00:01:18,733 And so there you go. 32 00:01:18,733 --> 00:01:22,433 I suggest that we really tackle in a flash light. 33 00:01:22,533 --> 00:01:24,300 I love seeing this I know but 34 00:01:24,300 --> 00:01:28,266 we will tackle in one second that data preprocessing phase. 35 00:01:28,500 --> 00:01:32,400 Except that part because that part is actually not that direct. 36 00:01:32,400 --> 00:01:35,400 You know, there are going to be some things to explain. 37 00:01:35,533 --> 00:01:36,500 All right. So let's do this. 38 00:01:36,500 --> 00:01:38,366 Let's start by importing the libraries. 39 00:01:38,366 --> 00:01:42,033 And of course to do this we're going to use our data preprocessing template. 40 00:01:42,033 --> 00:01:44,666 I hope you have it prepared. So there you go. 41 00:01:44,666 --> 00:01:48,133 Let's first get this code 42 00:01:48,166 --> 00:01:53,333 to import the libraries in a new code cell base here. 43 00:01:53,933 --> 00:01:55,733 Then we're going to import the data set. 44 00:01:55,733 --> 00:02:00,133 And actually to do this we'll get our polynomial regression implementation. 45 00:02:00,133 --> 00:02:02,466 Because you know this is the exact same data set. 46 00:02:02,466 --> 00:02:07,500 And we don't have to explain that again because you perfectly know and understand. 47 00:02:07,500 --> 00:02:10,366 Now how this works. Right. 48 00:02:10,366 --> 00:02:13,466 And well, that's the thing I wanted to do in one second. 49 00:02:13,766 --> 00:02:17,400 Now we're going to quickly upload the data set in order 50 00:02:17,400 --> 00:02:20,400 to, you know, execute the cell after this one, of course. 51 00:02:20,733 --> 00:02:24,966 So now it's connecting to a runtime to enable file browsing. 52 00:02:25,300 --> 00:02:29,700 And in a second we should be able to see the upload button. 53 00:02:29,700 --> 00:02:31,800 Perfect. So upload. 54 00:02:31,800 --> 00:02:36,833 And then as usual we're going to go into our machine learning is that folder 55 00:02:36,833 --> 00:02:41,000 I like to put it on my desktop but find it wherever you put it on your computer. 56 00:02:41,366 --> 00:02:46,000 Then forward to regression then support vector regression and Python. 57 00:02:46,266 --> 00:02:47,166 And there you go. 58 00:02:47,166 --> 00:02:52,766 That's the data set open and it will be uploaded inside the notebook. 59 00:02:52,800 --> 00:02:54,000 Perfect. 60 00:02:54,000 --> 00:02:57,000 All right then we're going to execute these two cells. 61 00:02:57,400 --> 00:02:59,133 And this one as well. 62 00:02:59,133 --> 00:03:01,100 All right. And perfect. 63 00:03:01,100 --> 00:03:02,833 Now we have the data set. 64 00:03:02,833 --> 00:03:05,300 And now we're going to stop here for this tutorial. 65 00:03:05,300 --> 00:03:09,000 And we will tackle this next step feature scaling in the next one. 66 00:03:09,300 --> 00:03:12,000 And make sure to be prepared and ready to tackle this 67 00:03:12,000 --> 00:03:13,700 because we'll have a few things to do. 68 00:03:13,700 --> 00:03:17,900 It's not difficult, but just make sure to be focused for that next tutorial, 69 00:03:17,933 --> 00:03:21,566 because I'm going to explain a new situation 70 00:03:21,800 --> 00:03:24,700 where you have to apply feature scaling in a certain way. 71 00:03:24,700 --> 00:03:27,033 So I'll see you in the next material. 72 00:03:27,033 --> 00:03:28,866 And until then, enjoy machine learning.