All language subtitles for 6. Types of Machine Learning

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These are the user uploaded subtitles that are being translated: 1 00:00:01,060 --> 00:00:02,500 Welcome back. 2 00:00:02,500 --> 00:00:09,580 Up until now I've been blabbering on about what machine learning is and I hope you have a bit of an 3 00:00:09,580 --> 00:00:14,050 idea of why we have it and why it's useful. 4 00:00:14,170 --> 00:00:21,040 But at the end of the day the only reason that we care about machine learning is that we're able to 5 00:00:21,040 --> 00:00:26,290 use machines to predict results based on incoming data. 6 00:00:26,290 --> 00:00:27,310 That's it. 7 00:00:27,340 --> 00:00:29,440 Now this idea of machine learning. 8 00:00:29,440 --> 00:00:30,240 And don't worry. 9 00:00:30,280 --> 00:00:35,740 I know we keep talking about theory but I promise we're gonna get some coding exercises first. 10 00:00:35,740 --> 00:00:43,330 But I do want to talk quickly about some of the machine learning categories that you often see and keep 11 00:00:43,330 --> 00:00:51,420 in mind what I said that is machine learning is simply about predicting results based on incoming data. 12 00:00:51,640 --> 00:00:55,820 And all these subcategories simply do that. 13 00:00:55,840 --> 00:01:03,820 For example we have the idea of a supervised learning which is a subset of machine learning in this 14 00:01:03,880 --> 00:01:05,270 supervised learning. 15 00:01:05,500 --> 00:01:09,400 The data that we received already has CATEGORIES. 16 00:01:09,400 --> 00:01:13,740 THINK OF IT AS A CSC files with rows and columns label. 17 00:01:13,840 --> 00:01:22,410 We have labeled data and a test data that is label so we know if our function is right or wrong. 18 00:01:22,480 --> 00:01:30,310 So in a supervised learning scenario we can do things like classification to decide is this an apple. 19 00:01:30,310 --> 00:01:38,020 Or is this a paper machine learning model simply draws a line to decide Hey this is an apple and this 20 00:01:38,110 --> 00:01:46,360 is a pair or we might do something called regression instead of classification based on inputs. 21 00:01:46,550 --> 00:01:54,680 For example predicting stock prices another way that we might use supervised learning is for example 22 00:01:54,680 --> 00:02:03,740 to hire engineers based on inputs based on years of experience based on maybe age maybe where they live 23 00:02:04,040 --> 00:02:11,810 what type of computers they have all these sorts of inputs that are labeled can be used in a supervised 24 00:02:11,870 --> 00:02:17,570 learning system to decide should I hire this engineer or should I not. 25 00:02:17,600 --> 00:02:25,370 Now sometimes we have data that doesn't have labels and this is called on supervised learning. 26 00:02:25,370 --> 00:02:32,690 Again think of it as a CSP file without perhaps the column names labeled sometimes with things like 27 00:02:32,690 --> 00:02:33,820 clustering. 28 00:02:33,980 --> 00:02:39,170 We need to create these groups or at least the machine to create these groups. 29 00:02:39,200 --> 00:02:45,490 For example we give it a bunch of data points and then the machine decides oh this is a group. 30 00:02:45,530 --> 00:02:52,550 This is a group and this is a group or we can have something like association rule learning where we 31 00:02:52,550 --> 00:03:00,890 associate different things to predict what a customer perhaps might buy in the future when groups don't 32 00:03:00,890 --> 00:03:01,760 exist. 33 00:03:01,760 --> 00:03:04,200 We call it unsupervised learning. 34 00:03:04,310 --> 00:03:10,910 We can't tell the machine that they are right or wrong like we can when we do apples versus pears since 35 00:03:11,000 --> 00:03:17,050 there are no true categories but we let the machines just create these categories for us. 36 00:03:17,060 --> 00:03:24,010 Finally we have this idea of reinforcement learning and reinforcement learning is really interesting 37 00:03:24,040 --> 00:03:31,130 because it's all about teaching machines through trial and error through rewards and punishment so the 38 00:03:31,130 --> 00:03:38,930 program simply learns a game by playing it millions of times until well it gets the highest score it 39 00:03:38,930 --> 00:03:45,650 doesn't know what it's doing at first but then it tries to maximize the score and eventually figures 40 00:03:45,650 --> 00:03:51,270 out that hey maybe I should try and move where the ball is coming. 41 00:03:51,860 --> 00:03:58,250 So this is seen for skill acquisition or real time learning and you see it a lot in machine learning 42 00:03:58,250 --> 00:04:00,960 programs that play for example video games. 43 00:04:01,010 --> 00:04:08,210 But the idea here is that machine learning has different categories and different ways to accomplish 44 00:04:08,240 --> 00:04:17,830 its goal and topics like neural networks decision trees support vector machines K nearest neighbor are 45 00:04:17,830 --> 00:04:27,580 simply algorithms that are used with these sub fields in order to come to these predictions but remember 46 00:04:27,820 --> 00:04:35,650 the key thing all of these what they're doing is trying to learn from the data that it receives and 47 00:04:35,650 --> 00:04:40,570 predict something I'll see in the next one by. 5398

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