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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:12,330 --> 00:00:14,310 Hi, everyone, and welcome in this new video. 2 00:00:14,940 --> 00:00:20,720 In this video, we're all going to see the main point of this chapter, which is the training of our 3 00:00:20,730 --> 00:00:27,930 linear regression, though we have already seen the theory behind the linear regression. 4 00:00:28,290 --> 00:00:40,310 And even for those of you which are not very comfortable with this notion, you will see that the practice, 5 00:00:40,320 --> 00:00:43,350 it's really much easier. 6 00:00:46,190 --> 00:00:56,030 To create a linear regression, we need to import the linear regression class from psychedelia to do 7 00:00:56,030 --> 00:00:56,300 it. 8 00:00:56,870 --> 00:01:00,900 We use the from import operator. 9 00:01:00,930 --> 00:01:13,160 So from psychic points linear model, we want to import the class linear regression. 10 00:01:19,950 --> 00:01:24,060 Then we need to initialize the class to do it. 11 00:01:24,090 --> 00:01:36,180 We're going to create a variable containing this class, and I have chosen to begin this course with 12 00:01:37,140 --> 00:01:44,750 their first model as a linear regression because we don't need to specify some parameter. 13 00:01:44,850 --> 00:01:54,360 So it's really much easier to you to understand your first machine learning algorithm so you don't need 14 00:01:54,360 --> 00:02:03,720 to put any parameters because the few parameters for this class is set by default. 15 00:02:04,560 --> 00:02:12,990 And then to fit the model, we just needed to use the function of the ranking class. 16 00:02:13,620 --> 00:02:26,490 So in parameters, we need to give the feature so extreme and the target because a linear regression 17 00:02:26,490 --> 00:02:29,190 is a supervised machine learning model. 18 00:02:29,490 --> 00:02:31,890 So it means that to between 19 00:02:35,010 --> 00:02:42,510 the algorithm needed extremes of the features and the target to compute and never function, and then 20 00:02:43,410 --> 00:02:48,760 change its parameters to minimize this error. 21 00:02:48,780 --> 00:02:58,360 So we need to put the target and then we are currently train or algorithm. 22 00:02:58,680 --> 00:03:07,020 And in the next video, we're going to do some prediction, some stock price prediction using this linear 23 00:03:07,020 --> 00:03:07,800 regression. 2462

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