WEBVTT 00:00.110 --> 00:00.770 Hey, guys. 00:00.770 --> 00:04.250 Welcome to day 31 of 100 Days of Code. 00:04.250 --> 00:07.040 And you've been learning quite a lot of things. 00:07.040 --> 00:15.050 Now, we've been looking at handling exceptions using the JSON data format, parsing and reading Csvs, 00:15.050 --> 00:20.090 using pandas, opening and writing to files, and a whole lot more. 00:20.090 --> 00:24.530 So it's time for your capstone project. 00:24.590 --> 00:30.470 And in this capstone project, we're going to be building a flashcard program to help you study. 00:30.470 --> 00:34.610 And it's especially great with studying for languages. 00:34.730 --> 00:42.470 Now, when I was in school, I studied French, and it was a lot of vocab that we had to learn for tests. 00:42.470 --> 00:48.710 Pomme is apple and I would memorize all these words, all of the grammar tables. 00:48.710 --> 00:53.450 And yet after 4 or 5 years, I couldn't really speak much French. 00:53.450 --> 00:58.160 So I decided to go for immersive language learning. 00:58.160 --> 01:07.370 I went to France, I hung out with friends and I tried to immerse myself in the language and culture, 01:07.430 --> 01:09.230 but that also failed. 01:09.230 --> 01:13.940 I had a lot of fun, but my French didn't seem to improve all that much. 01:14.120 --> 01:22.280 But then I discovered a new way of learning languages, and it all started with looking at Chinese characters. 01:22.340 --> 01:24.650 There's a lot of Chinese characters out there. 01:24.680 --> 01:31.700 There's something like 50,000 Chinese characters in total from history to now. 01:31.730 --> 01:36.140 There's a lot of characters that you could learn that each have a different meaning, and they each 01:36.140 --> 01:38.030 have a different pronunciation. 01:38.120 --> 01:39.200 Imagine that. 01:39.200 --> 01:43.310 Trying to learn 50,000 characters, that's no easy feat. 01:43.310 --> 01:49.190 But then a friend told me that actually, you don't really need 50,000 characters. 01:49.190 --> 01:55.790 Your average professor who is very eloquent, who can write a lot of the characters and use them with 01:55.790 --> 02:04.010 ease, only knows about 10,000, and your average person probably only uses about 8000 in their day 02:04.010 --> 02:05.150 to day lives. 02:05.210 --> 02:11.600 And if you basically just want to get by in life, you can pretty much rely on the 3000 words that an 02:11.600 --> 02:13.310 average teenager would know. 02:13.460 --> 02:19.550 And finally, if you actually just want to be able to watch some simple movies, read some simple books, 02:19.550 --> 02:24.560 then you could use the average kid vocabulary of about 1000 characters. 02:24.590 --> 02:27.590 And at this point, I think to myself, 1000. 02:27.620 --> 02:28.760 That's quite doable. 02:28.760 --> 02:32.420 I could do 1000 if I learned just ten characters a day. 02:32.450 --> 02:35.840 That will take me less than a year to learn all of these characters. 02:36.230 --> 02:40.190 But it's not just 1000 random characters either. 02:40.220 --> 02:42.860 There's such a thing as a frequency dictionary. 02:42.860 --> 02:49.550 So a dictionary that's not listed by A, B, C, D, but it's actually listed by the frequency that 02:49.550 --> 02:52.640 a particular word occurs in common usage. 02:52.910 --> 03:00.980 For example, if you take the first 1000 characters that are most commonly used, then you can pretty 03:00.980 --> 03:03.110 much read most of the newspapers. 03:03.110 --> 03:08.600 You can watch most of the TV shows because these are the words that are the bread and butter of the 03:08.600 --> 03:09.410 language. 03:09.410 --> 03:13.910 It's like in English the a, the of from y. 03:13.940 --> 03:14.540 Yes, no. 03:14.570 --> 03:18.140 These are words that we use every day again and again. 03:18.170 --> 03:24.410 The crazy words like anti-establishment or glioblastoma. 03:24.440 --> 03:29.150 These are not words that you need to really know for day to day life. 03:29.540 --> 03:34.310 So let me show you the program that you will build where you can learn the most frequently used words 03:34.310 --> 03:35.750 in any language. 03:35.780 --> 03:41.900 It's a flashcard program, and it shows you the front and the back of the card. 03:41.900 --> 03:47.300 So for example, French demand in English means request. 03:47.450 --> 03:53.990 After three seconds, the card flips and I can check whether if I knew the right answer, if I got it 03:53.990 --> 03:56.000 right, I'll press the tick. 03:56.000 --> 03:58.670 And if I got it wrong, I'll press the cross. 03:58.700 --> 04:00.500 So let's try another word. 04:00.500 --> 04:07.580 Party means Left or to leave a train means to wait. 04:07.580 --> 04:09.950 And I think I knew that word. 04:09.950 --> 04:12.560 So I'm going to click the check mark. 04:12.590 --> 04:17.960 And what that's going to do is it's going to take the flash card out of all of the list of flash cards. 04:17.960 --> 04:24.110 So it doesn't show me the things I already know, and instead it only shows me the things I don't know 04:24.110 --> 04:28.790 so I can review it and say, oh, I'm not sure what LA means. 04:28.790 --> 04:35.690 So I'll say cross, and that will go back into the deck and it might come up again at some point. 04:35.750 --> 04:40.610 So this beautiful piece of software is what we're going to be creating. 04:40.610 --> 04:47.120 But more specifically you're going to be creating because after all, this is your capstone project. 04:47.120 --> 04:53.510 But don't worry, I've divided up into four steps and I've got some step by step instructions for you 04:53.510 --> 04:55.640 in the next lesson. 04:55.970 --> 05:00.920 Now, if you're wondering, how did you get the most frequent words for the flashcard app in the first 05:00.920 --> 05:01.460 place? 05:01.460 --> 05:09.560 Well, let me show you There's a wiki for the frequency lists of different languages, and it lists 05:09.560 --> 05:11.330 most of the common languages. 05:11.330 --> 05:17.720 If we go to French, you can see that there are loads of different lists that people have compiled that 05:17.720 --> 05:21.290 list the top most frequently occurring words. 05:21.290 --> 05:27.860 And one of the ones that I thought was really relevant is the words that are based on subtitles. 05:27.890 --> 05:34.550 These subtitles come from all sorts of shows and movies that are relevant to modern culture. 05:34.550 --> 05:39.470 And when you look at one of the subtitles, this is one of my favorite shows, by the way. 05:39.470 --> 05:47.090 You can see that the subtitles are listed by language, and if we pick out one, which is in English 05:47.090 --> 05:54.800 and we take a look at it, then you can see it's basically just all the words that are spoken in the 05:54.800 --> 05:59.270 movie or in the show, and it's been transcribed into subtitles. 05:59.300 --> 06:05.190 Now then, if we take all of these words that are from the most commonly watched movies and shows. 06:05.190 --> 06:07.740 We end up with these frequency lists. 06:07.740 --> 06:15.960 So if we take a look here, it shows the most frequent words from 1 to 5000. 06:15.960 --> 06:20.190 And at the very beginning it's your eye of is. 06:20.220 --> 06:22.350 All of these things are really common. 06:22.350 --> 06:25.770 And then as you scroll down, you get to some longer words. 06:25.770 --> 06:31.710 And if you scroll to the bottom, you can see you're getting some more and more niche words. 06:32.370 --> 06:42.360 These frequency lists are compiled by a user called hermit D, and hermit D is Hermit Dave, and he 06:42.360 --> 06:48.570 has a GitHub repository where he's compiled all of the frequency words. 06:48.630 --> 06:52.530 And you can see the latest version from 2018. 06:52.560 --> 06:59.070 Now he's got all of the frequency words for many languages, and it's listed by the language code. 06:59.070 --> 07:02.130 So French would be fr for example. 07:02.160 --> 07:09.600 And here you can see the top 50,000 most frequent lists or the full entire list. 07:09.630 --> 07:13.470 We're probably not going to learn more than 1000. 07:13.500 --> 07:16.410 And I'm certainly not going to get to 50,000. 07:16.410 --> 07:22.770 But this data here lists all the words that he found in these subtitles and the frequency that they 07:22.770 --> 07:23.550 occurred. 07:23.580 --> 07:29.220 And once they've been sorted in order of frequency, this is what you end up with. 07:30.540 --> 07:33.930 So I've already studied some of the first 200 words. 07:33.930 --> 07:40.800 So if I take 100 words from this frequency dictionary and I put it into a Google sheet, then I end 07:40.830 --> 07:42.450 up with something like this. 07:42.600 --> 07:48.840 Now what I want to be able to do is to create a flashcard where the front of the flashcard is the word 07:48.840 --> 07:55.470 in French, and then on the back of the flashcard is the answer in English for what that word means. 07:55.680 --> 08:01.080 Instead of having to flip through a dictionary, finding out the meaning of each of these words, there's 08:01.110 --> 08:04.200 actually a really neat trick in Google Sheets that I want to show you. 08:04.290 --> 08:13.170 if you hit equals to start a new formula and you type in Google Translate, you can see it expects some 08:13.170 --> 08:13.920 inputs. 08:13.920 --> 08:16.800 First is the piece of text that you want to translate. 08:16.800 --> 08:18.690 So I'm going to click on this cell. 08:18.690 --> 08:20.760 And then it's the source language. 08:20.760 --> 08:23.490 So this is the language as a code. 08:23.490 --> 08:28.680 So for example Spanish is es and French is fr. 08:28.710 --> 08:34.440 And then the final input it expects is the language code that you want to translate it to. 08:34.470 --> 08:37.320 So in this case I want to translate it to English. 08:37.320 --> 08:41.310 So I'm going to use n and then we can close off the parentheses. 08:41.310 --> 08:42.150 Hit enter. 08:42.150 --> 08:47.730 And after a little while with good internet you'll see the English translation for this word. 08:47.730 --> 08:53.910 And of course because we're in Excel, we can simply just drag this across all the way down to all of 08:53.910 --> 08:54.630 our words. 08:54.630 --> 08:57.120 And after a little while, bam! 08:57.120 --> 09:00.900 It's translated all of those words into English. 09:01.380 --> 09:07.320 So this is a really neat trick, and I'll link to the docs for this particular formula. 09:07.380 --> 09:13.830 And also you can take a look at the language support that Google's translation service has. 09:13.860 --> 09:17.370 And you can see the language code for each of these languages. 09:17.370 --> 09:24.000 So if you want to try learning Macedonian or Malay, then this is going to be your best bet. 09:24.030 --> 09:31.470 So now that I've created my Excel sheet essentially of French and English words, I've got potentially 09:31.470 --> 09:38.010 100 flashcards with the front and back data already saved inside this Google sheet. 09:38.040 --> 09:45.450 Now all I have to do is simply download it as a CSV, and we'll be able to work with it very easily. 09:45.750 --> 09:51.570 Now you don't have to worry about downloading this or getting hold of this, because I've already included 09:51.570 --> 09:56.760 the final CSV data in the starting project for you to be able to use. 09:57.180 --> 10:05.040 So head over to the next lesson and get started building your very own study aid the flashy flashcard 10:05.070 --> 10:05.670 app.