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These are the user uploaded subtitles that are being translated: 1 00:00:02,720 --> 00:00:06,645 From the videos, you've seen supervised learning and 2 00:00:06,645 --> 00:00:10,000 unsupervised learning and also examples of both. 3 00:00:10,000 --> 00:00:13,500 For you to more deeply understand these concepts, 4 00:00:13,500 --> 00:00:16,560 I'll like to invite you in this class to see, 5 00:00:16,560 --> 00:00:18,930 learn and maybe later write codes 6 00:00:18,930 --> 00:00:21,615 yourself to implement these concepts. 7 00:00:21,615 --> 00:00:24,480 The most widely used tool by machine learning and 8 00:00:24,480 --> 00:00:27,910 data science practitioners today is the Jupyter Notebook. 9 00:00:27,910 --> 00:00:30,980 This is the default environments that a lot of us 10 00:00:30,980 --> 00:00:34,585 use to code up and experiment and try things out. 11 00:00:34,585 --> 00:00:38,210 In this class, right here in your web browser, 12 00:00:38,210 --> 00:00:41,060 you build a user Jupyter Notebook environment 13 00:00:41,060 --> 00:00:44,515 to test out some of these ideas for yourself as well. 14 00:00:44,515 --> 00:00:48,245 This is not some made up simplified environment, 15 00:00:48,245 --> 00:00:50,479 this is the exact same environments, 16 00:00:50,479 --> 00:00:53,345 the exact same tool, the Jupyter Notebook 17 00:00:53,345 --> 00:00:54,860 that developers are using in 18 00:00:54,860 --> 00:00:56,890 many large countries right now. 19 00:00:56,890 --> 00:00:59,180 One type of lab that you see throughout 20 00:00:59,180 --> 00:01:01,130 this class are optional labs, 21 00:01:01,130 --> 00:01:04,340 which are ones you can open and run one line at 22 00:01:04,340 --> 00:01:09,235 a time with usually no need to write any code yourself. 23 00:01:09,235 --> 00:01:13,175 Optional labs are designed to be very easy 24 00:01:13,175 --> 00:01:16,370 and I can guarantee you will get full marks, 25 00:01:16,370 --> 00:01:19,535 every single one of them because there are no marks. 26 00:01:19,535 --> 00:01:21,650 All you need to do is open it up 27 00:01:21,650 --> 00:01:24,220 and just run the code we've provided. 28 00:01:24,220 --> 00:01:26,150 By reading through and running 29 00:01:26,150 --> 00:01:27,740 the code in the optional labs, 30 00:01:27,740 --> 00:01:31,255 you see how machine learning code runs. 31 00:01:31,255 --> 00:01:35,090 You should complete them relatively 32 00:01:35,090 --> 00:01:37,085 quickly just by running it 33 00:01:37,085 --> 00:01:39,875 one line at a time from top to bottom. 34 00:01:39,875 --> 00:01:42,440 Optional labs are completely optional 35 00:01:42,440 --> 00:01:43,550 so you don't have to do 36 00:01:43,550 --> 00:01:45,370 them at all if you don't want to, 37 00:01:45,370 --> 00:01:47,915 but I hope you will take a look 38 00:01:47,915 --> 00:01:49,550 because running through them 39 00:01:49,550 --> 00:01:50,960 will give you a deeper feel, 40 00:01:50,960 --> 00:01:53,015 give you a little bit more experience 41 00:01:53,015 --> 00:01:55,115 with what machine learning algorithms, 42 00:01:55,115 --> 00:01:58,265 what machine learning code actually looks like. 43 00:01:58,265 --> 00:02:00,410 Starting next week, there'll also be 44 00:02:00,410 --> 00:02:02,630 some practice labs which would give you 45 00:02:02,630 --> 00:02:04,610 an opportunity to write some of that code 46 00:02:04,610 --> 00:02:07,190 yourself but we'll get to that next week. 47 00:02:07,190 --> 00:02:08,250 Don't worry about it for 48 00:02:08,250 --> 00:02:10,325 now and I hope you just go through 49 00:02:10,325 --> 00:02:12,230 the next optional lab and get 50 00:02:12,230 --> 00:02:14,960 through the rest of the content for this week. 51 00:02:14,960 --> 00:02:18,260 Let's take a look at an example of a notebook. 52 00:02:18,260 --> 00:02:19,860 Here's what you see when you 53 00:02:19,860 --> 00:02:21,870 go to the first optional lab. 54 00:02:21,870 --> 00:02:25,820 Feel free to scroll up and down and browse and mouseover 55 00:02:25,820 --> 00:02:27,620 the different menus and take 56 00:02:27,620 --> 00:02:30,520 a look at the different options here. 57 00:02:30,520 --> 00:02:32,450 You might notice that there are 58 00:02:32,450 --> 00:02:34,160 two types of these blocks, 59 00:02:34,160 --> 00:02:36,080 also called cells in 60 00:02:36,080 --> 00:02:39,475 the notebook and there are two types of cells. 61 00:02:39,475 --> 00:02:43,365 One is what's called a Markdown cell, 62 00:02:43,365 --> 00:02:46,035 which means a bunch of tax. 63 00:02:46,035 --> 00:02:48,260 Here you can actually edit the text 64 00:02:48,260 --> 00:02:50,785 if you don't like the text that we wrote, 65 00:02:50,785 --> 00:02:54,540 but this is text that describes the code. 66 00:02:54,540 --> 00:02:57,245 Then there's a second type of block 67 00:02:57,245 --> 00:02:59,900 or cell which looks like this, 68 00:02:59,900 --> 00:03:02,335 which has a code cell. 69 00:03:02,335 --> 00:03:04,770 Here, we've already provided the code 70 00:03:04,770 --> 00:03:07,160 and if you want to run this code cell, 71 00:03:07,160 --> 00:03:09,470 hitting Shift Enter will run 72 00:03:09,470 --> 00:03:12,695 the code in this code cell, and by the way, 73 00:03:12,695 --> 00:03:14,735 if you click on a markdown cell, 74 00:03:14,735 --> 00:03:17,300 so this showing all this formatting, 75 00:03:17,300 --> 00:03:20,780 go ahead and hit Shift Enter on your keyboard as 76 00:03:20,780 --> 00:03:22,805 well and that will also convert 77 00:03:22,805 --> 00:03:25,765 back to this nicely formatted text. 78 00:03:25,765 --> 00:03:29,300 This optional lab shows some common Python code, 79 00:03:29,300 --> 00:03:31,010 so you can go ahead and run this 80 00:03:31,010 --> 00:03:33,835 afterwards in your own Jupyter notebook. 81 00:03:33,835 --> 00:03:36,360 When you jump into this notebook yourself, 82 00:03:36,360 --> 00:03:38,270 what I'd like you to do is select 83 00:03:38,270 --> 00:03:41,320 the cells and hit Shift Enter. 84 00:03:41,320 --> 00:03:44,010 Read through the code, see if it makes sense, 85 00:03:44,010 --> 00:03:46,100 try to make a prediction about what you 86 00:03:46,100 --> 00:03:48,710 think this code would do and then 87 00:03:48,710 --> 00:03:51,080 hit Shift Enter and 88 00:03:51,080 --> 00:03:53,465 then see what the code actually does, 89 00:03:53,465 --> 00:03:54,980 and if you like it, 90 00:03:54,980 --> 00:03:57,125 feel free to go in and edit the code, 91 00:03:57,125 --> 00:04:00,415 change the code, and then run it and see what happens. 92 00:04:00,415 --> 00:04:02,010 If you haven't played in 93 00:04:02,010 --> 00:04:04,010 the Jupyter Notebook environment for, 94 00:04:04,010 --> 00:04:05,840 I hope you become more familiar with 95 00:04:05,840 --> 00:04:08,330 Python in a Jupyter Notebook. 96 00:04:08,330 --> 00:04:10,790 I spend a lot of hours playing around in 97 00:04:10,790 --> 00:04:12,470 Jupyter notebooks and so I 98 00:04:12,470 --> 00:04:14,680 hope you have fun with them too. 99 00:04:14,680 --> 00:04:17,090 After that, I look forward to seeing you 100 00:04:17,090 --> 00:04:19,160 in the next video where we'll take 101 00:04:19,160 --> 00:04:22,070 the supervised learning problem as start to flesh 102 00:04:22,070 --> 00:04:25,535 out our first supervised learning algorithm. 103 00:04:25,535 --> 00:04:26,960 I hope that will be fun to you and 104 00:04:26,960 --> 00:04:29,190 look forward to seeing you there.7506

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