1 00:00:00,433 --> 00:00:02,933 Welcome back to your Machine Learning A-To-Z course. 2 00:00:02,933 --> 00:00:04,900 In this tutorial, I'd like to give you a quick heads 3 00:00:04,900 --> 00:00:08,233 up about the non-linear support vector regression. 4 00:00:08,800 --> 00:00:12,400 So what we discussed previously was intuition for SVR. 5 00:00:12,400 --> 00:00:12,666 Indeed. 6 00:00:12,666 --> 00:00:17,400 But this was intuition of the linear support vector regression model. 7 00:00:17,733 --> 00:00:22,566 And while it's a great start into the world of SVR, it is just the start. 8 00:00:22,566 --> 00:00:25,266 There is so much more to support vector regression. 9 00:00:25,266 --> 00:00:28,166 this was a simple linear model. 10 00:00:28,166 --> 00:00:30,366 There are actually other models, 11 00:00:30,366 --> 00:00:34,533 and the rest assured they will be covered in this course. 12 00:00:34,633 --> 00:00:38,633 So they are coming up and we'll be talking about, 13 00:00:38,633 --> 00:00:43,266 for instance, the RBF kernel, SVR, and the kernel trick 14 00:00:43,266 --> 00:00:47,866 and all these important things that make this a model even more robust. 15 00:00:48,200 --> 00:00:51,500 However, I wanted to give you this heads up because already in 16 00:00:51,500 --> 00:00:55,066 upcoming Python tutorials that are coming right after. 17 00:00:55,300 --> 00:00:57,866 Yeah, just following this tutorial, in this section, 18 00:00:57,866 --> 00:01:01,033 you'll be working with had learned to create your first, 19 00:01:01,033 --> 00:01:05,900 SVR model, and the SVR model will already be nonlinear. 20 00:01:05,900 --> 00:01:07,400 So here you can see the results. 21 00:01:07,400 --> 00:01:09,266 Like I've blurred them out on purpose because, 22 00:01:10,800 --> 00:01:11,766 I wanted not to. 23 00:01:11,766 --> 00:01:13,200 I wanted to make sure you don't get, 24 00:01:13,200 --> 00:01:16,733 a spoiler by seeing the results in terms of the numbers, 25 00:01:17,100 --> 00:01:21,666 but you can see the model itself here, and you can see that it's non-linear. 26 00:01:21,666 --> 00:01:23,266 So just a straight line. 27 00:01:23,266 --> 00:01:26,700 And that is because we're using or at least will be showing you 28 00:01:26,700 --> 00:01:32,400 how to use, the radial basis function, kernel for this SVR. 29 00:01:32,400 --> 00:01:34,933 So it's really going to be a nonlinear SVR. 30 00:01:34,933 --> 00:01:37,133 So now what's the reason for this? 31 00:01:37,133 --> 00:01:37,500 heads up. 32 00:01:37,500 --> 00:01:43,300 Well, the thing is that the non linear support vector, regression 33 00:01:43,300 --> 00:01:47,333 will be kind of further down the course beyond this section. 34 00:01:47,633 --> 00:01:50,400 So it's a bit of a mismatch, but that's, done on purpose 35 00:01:50,400 --> 00:01:53,400 because that's the easier way to grasp these concepts. 36 00:01:53,666 --> 00:01:55,466 And I'll explain in this following slides. 37 00:01:55,466 --> 00:01:56,900 So this is what 38 00:01:58,400 --> 00:01:58,666 this is 39 00:01:58,666 --> 00:02:01,666 what the nonlinear SVR will look like. 40 00:02:01,666 --> 00:02:01,833 Right. 41 00:02:01,833 --> 00:02:02,900 So compared to this 42 00:02:02,900 --> 00:02:06,400 this is your linear model as you can see is much more complex over here. 43 00:02:06,400 --> 00:02:09,100 We have to go into a third dimension and perform 44 00:02:09,100 --> 00:02:11,700 some calculations or some modeling there and go back. 45 00:02:11,700 --> 00:02:13,133 So it is quite involved. 46 00:02:13,133 --> 00:02:17,666 And in order to get there, the following tutorials are essential. 47 00:02:17,666 --> 00:02:21,733 So first in the section on SVM you'll find a tutorial called SVM intuition. 48 00:02:21,866 --> 00:02:25,800 So that's an essential tutorial to take in the steps 49 00:02:25,800 --> 00:02:31,200 to getting to kernel SVR, or nonlinear SVR. 50 00:02:31,300 --> 00:02:35,100 Then in this section on kernel SVM, the following section, you 51 00:02:35,100 --> 00:02:38,166 can find them in the part of the course that is to do with classification. 52 00:02:38,400 --> 00:02:42,766 So in the section on kernel SVM there's a tool called kernel SVM intuition. 53 00:02:42,766 --> 00:02:44,900 Then there's mapping to a higher dimension. 54 00:02:44,900 --> 00:02:47,333 The kernel trick types of kernel function. 55 00:02:47,333 --> 00:02:50,566 And then finally nonlinear kernel SVR. 56 00:02:50,566 --> 00:02:53,400 So as you can see it's quite a complex topics topic. 57 00:02:53,400 --> 00:02:55,733 And it comes at the very end of this list. 58 00:02:55,733 --> 00:02:57,933 So from here you have two choices. 59 00:02:57,933 --> 00:03:02,133 You can either just after this tutorial go straight to advanced Python 60 00:03:02,133 --> 00:03:06,566 tutorials on kernel on the SVR there that are coming up and just do them. 61 00:03:06,733 --> 00:03:07,800 And just keep in mind 62 00:03:07,800 --> 00:03:12,200 that he's using a non-linear kernel, and that will come later in the course. 63 00:03:12,200 --> 00:03:13,666 The intuition for it will come later. 64 00:03:13,666 --> 00:03:14,333 And of course, 65 00:03:14,333 --> 00:03:17,566 or if you would like to get the intuition before you do 66 00:03:17,566 --> 00:03:18,800 the practical side of things, 67 00:03:18,800 --> 00:03:22,966 then it's essential that you would need to go through all of these tutorials 68 00:03:22,966 --> 00:03:24,933 that are mentioned on this slide over here. 69 00:03:24,933 --> 00:03:27,966 Get to non-linear kernel SVR and then come back to the Python terms. 70 00:03:28,200 --> 00:03:29,333 So there are two options. 71 00:03:29,333 --> 00:03:33,900 the my preference if you ask me I would just go for advanced Python tutorials. 72 00:03:33,900 --> 00:03:37,733 Now just keep in mind that he's using a non-linear kernel along with the code, 73 00:03:37,733 --> 00:03:41,466 and then you will still get the intuition when the time is right when you get to 74 00:03:41,466 --> 00:03:43,333 this section on kernel SVM. 75 00:03:43,333 --> 00:03:43,866 So there you go. 76 00:03:43,866 --> 00:03:46,066 That's just a quick heads up on what's coming up. 77 00:03:46,066 --> 00:03:47,433 of course the choice is yours. 78 00:03:47,433 --> 00:03:50,333 And in any case, please enjoy the course. 79 00:03:50,333 --> 00:03:52,900 And there's lots of fun tutorials coming up, 80 00:03:52,900 --> 00:03:54,300 and I look forward to seeing you next one. 81 00:03:54,300 --> 00:03:56,033 Until then, happy analyzing.