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These are the user uploaded subtitles that are being translated: 1 00:00:00,470 --> 00:00:02,910 In the last video, you saw 2 00:00:02,910 --> 00:00:04,695 what is unsupervised learning, 3 00:00:04,695 --> 00:00:08,025 and one type of unsupervised learning called clustering. 4 00:00:08,025 --> 00:00:11,220 Let's give a slightly more formal definition 5 00:00:11,220 --> 00:00:12,645 of unsupervised learning 6 00:00:12,645 --> 00:00:14,160 and take a quick look at 7 00:00:14,160 --> 00:00:15,405 some other types of 8 00:00:15,405 --> 00:00:17,640 unsupervised learning other than clustering. 9 00:00:17,640 --> 00:00:19,365 Whereas in supervised learning, 10 00:00:19,365 --> 00:00:21,915 the data comes with both inputs x and 11 00:00:21,915 --> 00:00:25,230 input labels y, in unsupervised learning, 12 00:00:25,230 --> 00:00:27,390 the data comes only with inputs 13 00:00:27,390 --> 00:00:29,850 x but not output labels y, 14 00:00:29,850 --> 00:00:32,085 and the algorithm has to find 15 00:00:32,085 --> 00:00:34,140 some structure or some pattern 16 00:00:34,140 --> 00:00:36,600 or something interesting in the data. 17 00:00:36,600 --> 00:00:39,440 We're seeing just one example of 18 00:00:39,440 --> 00:00:43,100 unsupervised learning called a clustering algorithm, 19 00:00:43,100 --> 00:00:45,805 which groups similar data points together. 20 00:00:45,805 --> 00:00:48,740 In this specialization, you'll learn about 21 00:00:48,740 --> 00:00:50,480 clustering as well as 22 00:00:50,480 --> 00:00:53,390 two other types of unsupervised learning. 23 00:00:53,390 --> 00:00:56,555 One is called anomaly detection, 24 00:00:56,555 --> 00:00:59,870 which is used to detect unusual events. 25 00:00:59,870 --> 00:01:02,570 This turns out to be really important for 26 00:01:02,570 --> 00:01:05,270 fraud detection in the financial system, 27 00:01:05,270 --> 00:01:08,690 where unusual events, unusual transactions could 28 00:01:08,690 --> 00:01:13,030 be signs of fraud and for many other applications. 29 00:01:13,030 --> 00:01:17,105 You also learn about dimensionality reduction. 30 00:01:17,105 --> 00:01:18,425 This lets you take 31 00:01:18,425 --> 00:01:21,860 a big data-set and almost magically compress it 32 00:01:21,860 --> 00:01:24,080 to a much smaller data-set while 33 00:01:24,080 --> 00:01:26,935 losing as little information as possible. 34 00:01:26,935 --> 00:01:29,360 In case anomaly detection and 35 00:01:29,360 --> 00:01:31,370 dimensionality reduction don't seem 36 00:01:31,370 --> 00:01:33,020 to make too much sense to you yet. 37 00:01:33,020 --> 00:01:34,670 Don't worry about it. We'll get to 38 00:01:34,670 --> 00:01:37,120 this later in the specialization. 39 00:01:37,120 --> 00:01:39,050 Now, I'd like to ask you 40 00:01:39,050 --> 00:01:43,040 another question to help you check your understanding, 41 00:01:43,040 --> 00:01:45,110 and no pressure, if you don't get it 42 00:01:45,110 --> 00:01:47,620 right on the first try, is totally fine. 43 00:01:47,620 --> 00:01:50,780 Please select any of the following 44 00:01:50,780 --> 00:01:54,200 that you think are examples of unsupervised learning. 45 00:01:54,200 --> 00:01:57,260 Two are unsupervised examples and two 46 00:01:57,260 --> 00:02:01,650 are supervised learning examples. Please take a look. 47 00:02:02,930 --> 00:02:06,670 Maybe you remember the spam filtering problem. 48 00:02:06,670 --> 00:02:09,085 If you have labeled data you now 49 00:02:09,085 --> 00:02:11,800 label as spam or non-spam e-mail, 50 00:02:11,800 --> 00:02:15,275 you can treat this as a supervised learning problem. 51 00:02:15,275 --> 00:02:18,175 The second example, the news story example. 52 00:02:18,175 --> 00:02:20,620 That's exactly the Google News and 53 00:02:20,620 --> 00:02:23,690 tangible example that you saw in the last video. 54 00:02:23,690 --> 00:02:26,200 You can approach that using 55 00:02:26,200 --> 00:02:29,515 a clustering algorithm to group news articles together. 56 00:02:29,515 --> 00:02:32,465 That we'll use unsupervised learning. 57 00:02:32,465 --> 00:02:35,020 The market segmentation example 58 00:02:35,020 --> 00:02:36,810 that I talked about a little bit earlier. 59 00:02:36,810 --> 00:02:38,350 You can do that as 60 00:02:38,350 --> 00:02:41,680 an unsupervised learning problem as well because you can 61 00:02:41,680 --> 00:02:44,195 give your algorithm some data and ask it 62 00:02:44,195 --> 00:02:47,600 to discover market segments automatically. 63 00:02:47,600 --> 00:02:51,620 The final example on diagnosing diabetes. 64 00:02:51,620 --> 00:02:53,420 Well, actually that's a lot like 65 00:02:53,420 --> 00:02:55,100 our breast cancer example 66 00:02:55,100 --> 00:02:57,775 from the supervised learning videos. 67 00:02:57,775 --> 00:03:00,980 Only instead of benign or malignant tumors, 68 00:03:00,980 --> 00:03:04,205 we instead have diabetes or not diabetes. 69 00:03:04,205 --> 00:03:07,130 You can approach this as a supervised learning problem, 70 00:03:07,130 --> 00:03:08,330 just like we did for the 71 00:03:08,330 --> 00:03:10,990 breast tumor classification problem. 72 00:03:10,990 --> 00:03:14,210 Even though in the last video, 73 00:03:14,210 --> 00:03:17,660 we've talked mainly about clustering, in later videos, 74 00:03:17,660 --> 00:03:20,720 in this specialization, we'll dive much more deeply into 75 00:03:20,720 --> 00:03:25,135 anomaly detection and dimensionality reduction as well. 76 00:03:25,135 --> 00:03:27,885 That's unsupervised learning. 77 00:03:27,885 --> 00:03:29,630 Before we wrap up this section, 78 00:03:29,630 --> 00:03:30,890 I want to share with you something 79 00:03:30,890 --> 00:03:32,540 that I find really exciting, 80 00:03:32,540 --> 00:03:34,340 and useful, which is the use of 81 00:03:34,340 --> 00:03:36,545 Jupyter Notebooks in machine learning. 82 00:03:36,545 --> 00:03:39,510 Let's take a look at that in the next video.5946

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