1 00:00:11,090 --> 00:00:16,400 So in this lecture, I want to talk about how to practice what you've learned in this course and how 2 00:00:16,400 --> 00:00:20,240 to make sure you are able to carry out the tasks you've been taught to do. 3 00:00:20,990 --> 00:00:26,900 Now, firstly, as mentioned in the introduction to this course, this is a hands on course. 4 00:00:27,170 --> 00:00:28,640 So what does this mean? 5 00:00:28,910 --> 00:00:33,710 This means that in this course you are strongly encouraged to get your hands dirty. 6 00:00:33,950 --> 00:00:40,130 Yes, you your actual hands typing on your keyboard, the things you learned in these lectures. 7 00:00:40,610 --> 00:00:44,090 So a few of you might be wondering, how does this work? 8 00:00:44,180 --> 00:00:46,370 So let's talk about how this works. 9 00:00:47,060 --> 00:00:51,230 Basically, the lectures in this course will proceed in a two step pattern. 10 00:00:51,440 --> 00:00:54,800 The first step will always be to discuss the concepts. 11 00:00:54,890 --> 00:00:56,030 What's going on? 12 00:00:56,030 --> 00:00:58,760 What are we trying to accomplish, how does it work? 13 00:00:58,760 --> 00:01:00,230 And so on and so forth. 14 00:01:00,260 --> 00:01:01,790 That's the first step. 15 00:01:02,240 --> 00:01:07,010 The second step will be to implement those ideas in actual working code. 16 00:01:07,280 --> 00:01:10,400 This is where getting your hands dirty comes into play. 17 00:01:11,240 --> 00:01:16,760 You see, what I want you to do is when you see these lectures, I want you to open your own a notebook 18 00:01:16,760 --> 00:01:22,640 or your coding environment and start typing out the stuff you see so that you learn how to implement 19 00:01:22,640 --> 00:01:24,170 these concepts yourself. 20 00:01:24,680 --> 00:01:30,230 Too often what I see is students just downloading notebooks and heading play, and what ends up happening 21 00:01:30,230 --> 00:01:35,540 is that they think they know what they're doing, but in practice they are completely incapable of writing 22 00:01:35,540 --> 00:01:36,920 any code themselves. 23 00:01:37,160 --> 00:01:41,030 And if you ask them, Do you know how to write this code, they will say yes. 24 00:01:41,180 --> 00:01:44,810 And then if you ask them, well, how much of this code did you actually write? 25 00:01:44,900 --> 00:01:46,490 The answer is none of it. 26 00:01:46,910 --> 00:01:51,470 And, you know, if you ever get a job or work in a project, that's what you have to do. 27 00:01:51,830 --> 00:01:56,210 You need to write actual code, not just believe in your mind that it can be done. 28 00:01:56,960 --> 00:02:02,390 The real world requires you to code, not to just watch other people code or download code that's already 29 00:02:02,390 --> 00:02:03,200 been written. 30 00:02:03,560 --> 00:02:07,430 The real world doesn't reward you for simply believing in yourself. 31 00:02:07,580 --> 00:02:11,000 The real world rewards you when you produce useful outputs. 32 00:02:11,090 --> 00:02:13,190 So that's what you're learning to do in this course. 33 00:02:13,190 --> 00:02:16,760 Produce useful outputs, not just thinking you can. 34 00:02:21,270 --> 00:02:24,720 Now, this course is mostly an API focused course. 35 00:02:24,810 --> 00:02:30,270 This is opposed to a more theoretical course where you might be taught an algorithm and then be expected 36 00:02:30,270 --> 00:02:32,670 to implement that algorithm from scratch. 37 00:02:33,180 --> 00:02:38,610 For this course being mostly an API focused course, you're not going to know the syntax until they 38 00:02:38,610 --> 00:02:39,960 actually show it to you. 39 00:02:40,380 --> 00:02:43,980 So in that sense, you will need to see the code as you code. 40 00:02:44,310 --> 00:02:50,100 I think this is important to mention since many of my courses are courses where you are taught algorithms 41 00:02:50,100 --> 00:02:53,220 and then you implement those algorithms as an exercise. 42 00:02:53,550 --> 00:02:56,370 This, however, is not one of those courses. 43 00:02:56,520 --> 00:03:01,560 In this course, you will need me to show you the syntax which you will be shown in the lectures. 44 00:03:05,980 --> 00:03:09,250 Now you might be wondering, isn't coding a mental thing? 45 00:03:09,280 --> 00:03:11,920 Why do I actually physically have to code? 46 00:03:12,250 --> 00:03:15,700 Isn't it enough to just see some syntax with my eyes? 47 00:03:16,060 --> 00:03:17,800 And, well, the answer is no. 48 00:03:17,830 --> 00:03:24,400 This has to do with a concept called muscle memory by typing your building muscle memory, which will 49 00:03:24,400 --> 00:03:26,920 help you produce similar code in the future. 50 00:03:27,700 --> 00:03:32,830 Now, for those of you who might be suspicious of this claim, please email me and I'll send you a link 51 00:03:32,830 --> 00:03:37,510 from a famous machine learning professor from Stanford who recently wrote about this. 52 00:03:37,810 --> 00:03:43,450 It is simply undeniable that this is good for your learning experience, and if you don't trust me, 53 00:03:43,450 --> 00:03:48,850 then you can at least trust this Stanford professor who is very well known in the machine learning community. 54 00:03:53,320 --> 00:03:58,180 Now, here's one thing I do not want you to do, which I've seen some students do in the past. 55 00:03:58,420 --> 00:03:59,880 And guys, please don't do this. 56 00:03:59,890 --> 00:04:01,000 It's a very silly. 57 00:04:01,240 --> 00:04:05,590 And also, if you do end up doing this, I'm going to know that you didn't pay close attention to this 58 00:04:05,590 --> 00:04:06,190 lecture. 59 00:04:07,270 --> 00:04:10,090 So what is this big mistake that I don't want you to do? 60 00:04:10,450 --> 00:04:14,050 I don't want you to type any data links you see by hand. 61 00:04:14,050 --> 00:04:18,190 And I don't want you to Google, search the links for the data sets in this course. 62 00:04:19,029 --> 00:04:24,970 So just to repeat that, do not type data links by hand and do not Google search the data links. 63 00:04:25,120 --> 00:04:26,050 Why? 64 00:04:26,140 --> 00:04:29,320 Because the data links are already given in this lecture. 65 00:04:29,710 --> 00:04:35,410 Just click the resources tab or go to the extra section and you will see a link to a document containing 66 00:04:35,410 --> 00:04:37,540 all the data links for this course. 67 00:04:37,900 --> 00:04:43,900 So yes, for the code portions, I do want you to type all of that by hand because that's actually useful. 68 00:04:43,930 --> 00:04:45,610 Typing code is useful. 69 00:04:45,760 --> 00:04:48,370 Typing data URLs is not so useful. 70 00:04:52,790 --> 00:04:58,580 Now, one thing you'll notice while watching the code lectures is that I myself do not type the code 71 00:04:58,580 --> 00:04:59,870 during these lectures. 72 00:05:00,260 --> 00:05:02,420 Of course, that's completely unnecessary. 73 00:05:02,900 --> 00:05:08,810 Some students in the past have made the mistake of thinking that because I wasn't typing that they also 74 00:05:08,810 --> 00:05:09,890 could not type. 75 00:05:10,160 --> 00:05:11,830 Of course, this is very silly. 76 00:05:11,840 --> 00:05:14,780 You don't need me to type in order for you to type. 77 00:05:14,930 --> 00:05:17,840 When you see any code, you can type that code. 78 00:05:17,990 --> 00:05:23,450 This allows me to focus on explaining things to you, which is more useful than just typing on the keyboard. 79 00:05:23,870 --> 00:05:28,450 And guys, just so we're clear, when you see a 20 minute lecture of just typing straight through, 80 00:05:28,460 --> 00:05:30,740 which is what you might find in some other course. 81 00:05:30,830 --> 00:05:34,760 This is not an accurate depiction of how that code was actually written. 82 00:05:35,330 --> 00:05:40,160 For a lot of this kind of code, sometimes it takes hours and maybe even days to finish. 83 00:05:40,310 --> 00:05:45,320 So if you think watching me type a notebook from beginning to end is going to give you some idea of 84 00:05:45,320 --> 00:05:47,510 how it's done live, it won't. 85 00:05:47,750 --> 00:05:52,460 It would simply lead you to a false impression of how to write code in the real world. 86 00:05:56,970 --> 00:06:03,000 Now, one question I get sometimes is that the code in the videos is too small or too blurry to see. 87 00:06:03,690 --> 00:06:10,050 So firstly, please note that this course has been optimised to be viewed on a desktop or laptop computer. 88 00:06:10,380 --> 00:06:15,180 It wasn't really designed to be viewed on, say, your smartphone because you can't simultaneously write 89 00:06:15,180 --> 00:06:19,500 Python code on your smartphone, at least not with today's smartphones. 90 00:06:19,860 --> 00:06:21,390 So that's tip number one. 91 00:06:21,510 --> 00:06:25,530 Make sure you are using a full sized screen and not a smartphone screen. 92 00:06:26,220 --> 00:06:30,360 Tip number two Note that you can change the quality of the video. 93 00:06:30,930 --> 00:06:33,880 For some students with slow Internet connections. 94 00:06:33,900 --> 00:06:38,430 This platform will automatically adjust the quality to have a lower resolution. 95 00:06:38,850 --> 00:06:42,240 The result is that the video looks blurry compared to the original. 96 00:06:42,720 --> 00:06:48,630 The original resolution is 1080 PPI, and you can select this manually from the video player. 97 00:06:48,960 --> 00:06:52,800 And if you don't know how to do this, please contact the technical support team. 98 00:06:53,520 --> 00:06:57,450 And finally, tip number three, make the video full screen. 99 00:06:57,900 --> 00:07:02,130 When you do this, the text actually becomes larger than it was on my own screen. 100 00:07:02,490 --> 00:07:07,590 I've also checked this myself and can confirm that the code you see in the videos is larger than the 101 00:07:07,590 --> 00:07:09,960 font size in the New York Times. 102 00:07:10,170 --> 00:07:15,180 So as long as you're able to view regular websites, then you should be able to view the code in these 103 00:07:15,180 --> 00:07:16,050 lectures. 104 00:07:20,890 --> 00:07:26,650 So aside from just notebooks, there is yet another place to get extra resources for this course. 105 00:07:26,770 --> 00:07:31,990 For example, reading materials, data processing scripts, and other miscellaneous things. 106 00:07:32,350 --> 00:07:39,730 To get these, please go to my GitHub repo GitHub slash lazy programmer slash machine learning examples. 107 00:07:39,880 --> 00:07:45,310 This contains materials for all my courses in a single repo and it's organized by folder. 108 00:07:45,580 --> 00:07:49,540 Essentially it's one folder per course, although there are some exceptions. 109 00:07:49,840 --> 00:07:55,030 In order to go directly to the folder for this course, please click the resources link attached to 110 00:07:55,030 --> 00:07:55,840 this lecture. 111 00:07:57,200 --> 00:08:03,200 Please note that the GitHub repo also contains a file called Extra Reading text, which includes links 112 00:08:03,200 --> 00:08:08,900 to papers, journal articles, tutorials, books and other useful resources for this course. 113 00:08:09,290 --> 00:08:15,080 Within this course I may refer to research papers, in which case links to these research papers can 114 00:08:15,080 --> 00:08:16,250 be found in this file. 115 00:08:16,280 --> 00:08:18,020 Extra Reading Text. 116 00:08:22,550 --> 00:08:27,320 Now if you're a new to Git or GitHub and you're a bit intimidated, please see the upcoming lecture 117 00:08:27,320 --> 00:08:30,920 called How to use GitHub and Extra Coding Tips.