1 00:00:01,390 --> 00:00:06,350 OK so we have an idea of what machine learning is kind of. 2 00:00:06,790 --> 00:00:08,320 But then there's other things. 3 00:00:08,320 --> 00:00:08,760 Right. 4 00:00:08,770 --> 00:00:14,990 Like A.I. Artificial Intelligence data science deep learning neural networks. 5 00:00:15,100 --> 00:00:17,470 What are all those things. 6 00:00:17,640 --> 00:00:23,590 Now let's look at machine learning and how it fits into some of the other words you may have heard. 7 00:00:23,670 --> 00:00:27,020 Now you have to keep this diagram in mind. 8 00:00:27,100 --> 00:00:35,650 You see it all starts with A.I. or artificial intelligence which simply means a human intelligence exhibited 9 00:00:35,740 --> 00:00:41,050 by machines and A.I. is a machine that acts like a human. 10 00:00:41,050 --> 00:00:47,650 And currently in our industry we have something called Narrow A.I. that is machines can be just as good 11 00:00:47,740 --> 00:00:51,700 or even better than humans at specific tasks. 12 00:00:51,700 --> 00:01:00,860 For example detecting heart disease from images or at a game of Go or chess or Starcraft and other video 13 00:01:00,860 --> 00:01:01,690 games. 14 00:01:01,750 --> 00:01:05,380 But each A.I. is only good at one task. 15 00:01:05,380 --> 00:01:13,000 Narrow A.I. that we currently have simply means those machines can only do one thing really well they 16 00:01:13,000 --> 00:01:16,240 can't be like humans and have multiple abilities. 17 00:01:16,240 --> 00:01:23,180 That's called General A.I. and it's something that we're very very far away from now machine learning 18 00:01:23,420 --> 00:01:32,030 is a subset of A.I. and machine learning is an approach to try and achieve artificial intelligence through 19 00:01:32,030 --> 00:01:35,810 systems that can find patterns in a set of data. 20 00:01:36,110 --> 00:01:42,740 And actually Stanford University describes machine learning as the science of getting computers to act 21 00:01:43,010 --> 00:01:49,580 without being explicitly programmed that is getting machines to do things without us specifically saying 22 00:01:49,940 --> 00:01:51,680 do this then do that. 23 00:01:51,680 --> 00:01:58,910 If then if this if this then that and don't worry we'll explain that more throughout the section. 24 00:01:58,910 --> 00:02:07,850 Now you may have also heard of deep learning and deep learning or deep neural networks is just one of 25 00:02:07,850 --> 00:02:11,510 the techniques for implementing machine learning. 26 00:02:11,510 --> 00:02:17,920 For now you can just think of it as a type of algorithm but then we have this other thing that we've 27 00:02:17,920 --> 00:02:18,970 heard of right. 28 00:02:19,000 --> 00:02:20,770 That's also very popular. 29 00:02:20,770 --> 00:02:29,400 That is data science and often the role of a data science and machine learning expert are quite overlapping. 30 00:02:30,160 --> 00:02:37,570 And most job descriptions actually don't even have a clear distinction between what is a machine learning 31 00:02:37,630 --> 00:02:40,180 expert and a data science expert. 32 00:02:40,180 --> 00:02:46,420 The field of data science simply means analyzing data looking at data and then doing something with 33 00:02:46,420 --> 00:02:49,460 it usually some sort of a business goal. 34 00:02:49,480 --> 00:02:54,670 So when we talk about machine learning there's a lot of overlap with data science and that's why this 35 00:02:54,670 --> 00:02:57,600 course is called machine learning and data science. 36 00:02:57,640 --> 00:03:00,910 If you are a data scientist you need to know machine learning. 37 00:03:00,910 --> 00:03:06,680 If you are a machine learning expert you need to know data science so throughout the next couple of 38 00:03:06,680 --> 00:03:12,020 videos we're going to be talking about both of these topics because both of them relate and overlap 39 00:03:12,920 --> 00:03:17,420 and you can't do one without the other because you need to be able to understand data and work with 40 00:03:17,420 --> 00:03:20,100 data to do any of these things. 41 00:03:20,120 --> 00:03:26,030 That's why when we talk about careers often things like machine learning data scientist data analyst 42 00:03:26,330 --> 00:03:31,250 they're all similar because they all work with data and from that data they want to derive some sort 43 00:03:31,250 --> 00:03:35,960 of action for their business for the company or for their users. 44 00:03:35,960 --> 00:03:39,980 Now in this course we're really focusing on the application side of things. 45 00:03:39,980 --> 00:03:44,260 That is the day to day use of machine learning and data science. 46 00:03:44,330 --> 00:03:51,650 We won't focus on theoretical academic research which means that you won't need a P H D Or be super 47 00:03:51,650 --> 00:03:55,910 smart mathematician or statistician to do the scores. 48 00:03:55,910 --> 00:04:03,620 The focus is on using machine learning and data science to be productive and to get job ready because 49 00:04:03,620 --> 00:04:05,960 that's what companies want right now. 50 00:04:05,960 --> 00:04:12,290 Now when Daniel starts introducing data science and machine learning he'll combine these two into one 51 00:04:12,950 --> 00:04:17,830 and move this machine learning circle inside of data science. 52 00:04:17,840 --> 00:04:23,680 That's because the goal of the course is to encompass this entire data science field and teach them 53 00:04:23,680 --> 00:04:31,130 machine learning aspects within data science by the way you might be wondering hey what about this whole 54 00:04:31,340 --> 00:04:33,100 data engineering thing that I've heard about. 55 00:04:33,110 --> 00:04:34,550 That's another term isn't it. 56 00:04:34,550 --> 00:04:35,210 What about that. 57 00:04:35,210 --> 00:04:37,010 Talk about that Andre. 58 00:04:37,010 --> 00:04:37,920 Well you know what. 59 00:04:37,970 --> 00:04:43,160 We have a whole section on it later on in the course of for now pretend like it doesn't exist. 60 00:04:43,160 --> 00:04:45,200 We'll get to it I promise. 61 00:04:45,200 --> 00:04:50,690 So now that we have an idea of what these big words are let's have some fun in the next video.