1 00:00:00,133 --> 00:00:00,866 Hello my friends. 2 00:00:00,866 --> 00:00:01,633 Welcome back. 3 00:00:01,633 --> 00:00:02,300 Are you ready. 4 00:00:02,300 --> 00:00:04,700 To finish and complete that cleaning of. 5 00:00:04,700 --> 00:00:05,833 All our reviews? 6 00:00:05,833 --> 00:00:06,600 Let's do this. 7 00:00:06,600 --> 00:00:09,033 So just to recap on what we did so far. 8 00:00:09,033 --> 00:00:11,033 We made this for loop to, you know. 9 00:00:11,033 --> 00:00:12,866 Treat each review separately. 10 00:00:12,866 --> 00:00:14,866 And clean each of them. Separately. 11 00:00:14,866 --> 00:00:15,600 And the first thing we. 12 00:00:15,600 --> 00:00:19,700 Did for each of these reviews was to replace all these. Non. 13 00:00:19,700 --> 00:00:22,366 Letters by spaces and basically all. 14 00:00:22,366 --> 00:00:24,833 The punctuations by. Spaces. 15 00:00:24,833 --> 00:00:27,600 Then we turned all the letters. 16 00:00:27,600 --> 00:00:28,800 Into. Lowercase. 17 00:00:28,800 --> 00:00:31,633 So all these capital letters were. Turned into. 18 00:00:31,633 --> 00:00:33,400 Lowercase. And finally. 19 00:00:33,400 --> 00:00:35,333 We splitted each review. 20 00:00:35,333 --> 00:00:37,600 Into its different words. Because now. 21 00:00:37,600 --> 00:00:38,900 We were about to proceed with. 22 00:00:38,900 --> 00:00:40,600 Stemming, which consists. 23 00:00:40,600 --> 00:00:41,800 Of. Simplifying. 24 00:00:41,800 --> 00:00:44,433 Each word by the root of the word. 25 00:00:44,433 --> 00:00:44,800 So for. 26 00:00:44,800 --> 00:00:47,800 Example. Loved will be replaced by love. 27 00:00:48,000 --> 00:00:49,800 And I remind that it's very important to. 28 00:00:49,800 --> 00:00:50,833 Do this to. 29 00:00:50,833 --> 00:00:54,533 Optimize the dimensionality of the sparse matrix. 30 00:00:54,533 --> 00:00:56,466 Which will be created through. The next. 31 00:00:56,466 --> 00:00:58,433 Step with the. Bag of words model. 32 00:00:58,433 --> 00:01:01,000 Indeed, we will create that sparse matrix where each. 33 00:01:01,000 --> 00:01:03,300 Column is. A. Word among all the. 34 00:01:03,300 --> 00:01:05,333 Different words of our different reviews after. 35 00:01:05,333 --> 00:01:09,000 The cleaning, and therefore we need to simplify it to the maximum. 36 00:01:09,000 --> 00:01:10,966 Well, the different. Words that are going to be. 37 00:01:10,966 --> 00:01:12,233 In this sparse matrix. 38 00:01:12,233 --> 00:01:14,433 So it's very. Very important to apply stemming. 39 00:01:14,433 --> 00:01:17,266 And that's. What we're about to do. Right now. 40 00:01:17,266 --> 00:01:17,633 All right. 41 00:01:17,633 --> 00:01:19,800 So remember we imported this. 42 00:01:19,800 --> 00:01:21,000 Porter Stemmer class. 43 00:01:21,000 --> 00:01:23,900 That's exactly the class that will apply stemming. 44 00:01:23,900 --> 00:01:25,300 And so now the first thing we'll do is. 45 00:01:25,300 --> 00:01:27,166 Actually. Create an object. 46 00:01:27,166 --> 00:01:31,533 Of this class, which we will call p s for, you know, for a steamer. And. 47 00:01:31,533 --> 00:01:33,800 Forest Emma is a particular type. Of stem here. 48 00:01:33,800 --> 00:01:36,766 You can check it out online. But that's. Basically the classic way. 49 00:01:36,766 --> 00:01:38,166 Of applying stemming. 50 00:01:38,166 --> 00:01:39,766 So and now. 51 00:01:39,766 --> 00:01:42,766 We will just call the class Porter. 52 00:01:42,900 --> 00:01:45,366 Stem nerd. Perfect. 53 00:01:45,366 --> 00:01:46,966 Then adding some parentheses. 54 00:01:46,966 --> 00:01:49,266 And we don't have to enter any parameters. 55 00:01:49,266 --> 00:01:51,600 So let me actually. Scroll down a bit. 56 00:01:51,600 --> 00:01:52,233 All right. 57 00:01:52,233 --> 00:01:54,233 This will be easier for you. 58 00:01:54,233 --> 00:01:56,533 So Porter. Stemmer now we. Have our object. 59 00:01:56,533 --> 00:01:59,666 So basically we have our tool to apply the stemming. 60 00:01:59,833 --> 00:02:00,933 And therefore well. 61 00:02:00,933 --> 00:02:03,300 The next. Step will be exactly to apply. 62 00:02:03,300 --> 00:02:05,500 Stemming to. This particular review. 63 00:02:05,500 --> 00:02:06,966 We're dealing with right now inside. 64 00:02:06,966 --> 00:02:09,266 This for loop iterating all the reviews. 65 00:02:09,266 --> 00:02:10,800 So the way we're going to do. 66 00:02:10,800 --> 00:02:12,666 This because you know, we have to apply stemming to. 67 00:02:12,666 --> 00:02:15,400 Each of the words. Inside. This review. 68 00:02:15,400 --> 00:02:20,000 And the words have already been gathered separately by splitting this review. 69 00:02:20,266 --> 00:02:22,000 And therefore what we're going to do now. 70 00:02:22,000 --> 00:02:24,433 Is first, we'll update our review. 71 00:02:24,433 --> 00:02:27,066 Again to become this time. 72 00:02:27,066 --> 00:02:30,133 A list of all these separate words. 73 00:02:30,166 --> 00:02:31,500 Coming from the. Split. 74 00:02:31,500 --> 00:02:33,033 But stepped, you know. 75 00:02:33,033 --> 00:02:35,633 After we applied. Stemming on them. 76 00:02:35,633 --> 00:02:38,600 And so now the question is, how are we going to apply stemming to. 77 00:02:38,600 --> 00:02:40,333 Each of the words of the review? 78 00:02:40,333 --> 00:02:44,100 Well, we're going to do that, of course, with a single row for loop, 79 00:02:44,100 --> 00:02:46,200 as I like to call it, you know, a full loop. 80 00:02:46,200 --> 00:02:47,600 Inside a same row. 81 00:02:47,600 --> 00:02:50,466 Which is, you know, usually applied to lists. 82 00:02:50,466 --> 00:02:51,100 And since now. 83 00:02:51,100 --> 00:02:53,700 We actually have a list of. The words in the reviews. 84 00:02:53,700 --> 00:02:54,400 Thanks to this. 85 00:02:54,400 --> 00:02:56,733 Steps. And here, well, we can totally apply. 86 00:02:56,733 --> 00:03:00,866 This single row for loop to iterate through all the words of the review. 87 00:03:00,866 --> 00:03:03,400 And apply stemming to each of them. 88 00:03:03,400 --> 00:03:05,300 So let's actually start with this for loop. 89 00:03:05,300 --> 00:03:10,366 We're going to start with four which will iterate through all the word you know. 90 00:03:10,366 --> 00:03:14,033 So word is just a iterated variable that will be equal to the. 91 00:03:14,033 --> 00:03:15,666 Different words of. The review. 92 00:03:15,666 --> 00:03:17,266 So for word. 93 00:03:17,266 --> 00:03:18,733 Then in. 94 00:03:18,733 --> 00:03:20,100 Review right. 95 00:03:20,100 --> 00:03:22,933 Because review is now a. List of different words. 96 00:03:22,933 --> 00:03:26,800 So it will iterate through all the different words of this review. 97 00:03:27,133 --> 00:03:29,400 And then we will add something which. 98 00:03:29,400 --> 00:03:30,933 Is probably. Something. You will see for the. 99 00:03:30,933 --> 00:03:33,133 First time if you're. New to Python. 100 00:03:33,133 --> 00:03:35,400 But here, after a single row for loop. 101 00:03:35,400 --> 00:03:36,133 We can. Actually. 102 00:03:36,133 --> 00:03:38,533 Add a condition to omit. 103 00:03:38,533 --> 00:03:41,433 Some of the words we are. Iterating through. 104 00:03:41,433 --> 00:03:43,966 And according. To you, what. Do we want to emit? 105 00:03:43,966 --> 00:03:46,800 You know, what do we want not to include in the full loop? 106 00:03:46,800 --> 00:03:49,433 Well, that's of. Course remember them. 107 00:03:49,433 --> 00:03:51,233 The stopwords. 108 00:03:51,233 --> 00:03:53,166 Right. Remember, we want to get rid. 109 00:03:53,166 --> 00:03:54,666 Of all the words that. 110 00:03:54,666 --> 00:03:58,300 Won't help us or, you know, that won't help the machine learning model 111 00:03:58,333 --> 00:03:59,433 to understand whether. 112 00:03:59,433 --> 00:04:01,700 A review is positive or negative. 113 00:04:01,700 --> 00:04:02,500 And these include. 114 00:04:02,500 --> 00:04:05,766 All the articles like the, Unlike an. 115 00:04:05,766 --> 00:04:07,800 Apple, we would just keep Apple, for. 116 00:04:07,800 --> 00:04:10,266 Example, and even all the pronouns, you know. 117 00:04:10,266 --> 00:04:12,233 Like I, you, he she. 118 00:04:12,233 --> 00:04:13,800 We, you know, then. 119 00:04:13,800 --> 00:04:15,733 They all these words give us. 120 00:04:15,733 --> 00:04:17,366 Absolutely no hint to. 121 00:04:17,366 --> 00:04:20,000 Figure out whether review is positive. Or negative. 122 00:04:20,000 --> 00:04:22,900 And therefore we want. To get rid of all the stopwords. 123 00:04:22,900 --> 00:04:24,166 And that's again for the same. 124 00:04:24,166 --> 00:04:25,966 Purpose as. Stemming. 125 00:04:25,966 --> 00:04:29,833 It is to simplify the final sparse matrix, which will contain 126 00:04:29,833 --> 00:04:30,900 all the different words of. 127 00:04:30,900 --> 00:04:33,000 All our. Reviews into columns. 128 00:04:33,000 --> 00:04:36,566 So we not only want to simplify the different versions of words 129 00:04:36,566 --> 00:04:39,366 or, you know, branches of words by only keeping their root. But. 130 00:04:39,366 --> 00:04:40,400 Also we want to get with. 131 00:04:40,400 --> 00:04:41,366 All the stopwords. 132 00:04:41,366 --> 00:04:44,300 In order not to include them in the sparse matrix. 133 00:04:44,300 --> 00:04:46,666 And a way to do this, you know, the way to omit. 134 00:04:46,666 --> 00:04:47,466 These words. 135 00:04:47,466 --> 00:04:50,233 When iterating through the different words in the review. 136 00:04:50,233 --> 00:04:52,633 Is by just. Adding. If, here. 137 00:04:52,633 --> 00:04:56,033 And then and not, you know you will understand after I finished this. 138 00:04:56,033 --> 00:04:57,500 So let me just write it. 139 00:04:57,500 --> 00:05:00,366 If not, word in. 140 00:05:00,366 --> 00:05:02,700 The set up. 141 00:05:02,700 --> 00:05:03,233 Then I'm going to. 142 00:05:03,233 --> 00:05:05,700 Call the Stopwords module here. 143 00:05:05,700 --> 00:05:07,300 You know this stopwords here. 144 00:05:07,300 --> 00:05:10,100 From which I'm going to get all the English. 145 00:05:10,100 --> 00:05:12,566 Stopwords because. You know, our reviews are in English. 146 00:05:12,566 --> 00:05:15,366 And the way to. Do this is to add here words. 147 00:05:15,366 --> 00:05:17,966 And then in parentheses because that's a function. 148 00:05:17,966 --> 00:05:20,966 And then in quotes English okay. 149 00:05:21,166 --> 00:05:23,633 So we clearly. Understand what this means. 150 00:05:23,633 --> 00:05:26,900 It means that. If the word of the review. 151 00:05:27,000 --> 00:05:28,566 You know we're dealing with right now in this. 152 00:05:28,566 --> 00:05:31,000 For loop, if this word is. 153 00:05:31,000 --> 00:05:34,000 Not in the set, you know, the symbol. 154 00:05:34,033 --> 00:05:37,366 Of all the English stopwords. Well, we. 155 00:05:37,366 --> 00:05:39,033 Will consider it in this for loop. 156 00:05:39,033 --> 00:05:41,400 And then we will. Apply the. Stemming. 157 00:05:41,400 --> 00:05:44,300 However, if this word is. In all. 158 00:05:44,300 --> 00:05:46,200 These stopwords list, well. 159 00:05:46,200 --> 00:05:49,500 We won't include it in this for loop and therefore we won't apply the stemming 160 00:05:49,500 --> 00:05:49,900 to. It. 161 00:05:49,900 --> 00:05:52,800 Therefore it won't. Be in the future. Sparse matrix. 162 00:05:52,800 --> 00:05:54,566 So you see the trick. Pretty cool. 163 00:05:54,566 --> 00:05:55,300 But so. 164 00:05:55,300 --> 00:05:57,666 Far we only have the for loop and we don't know what we're going to. 165 00:05:57,666 --> 00:06:00,600 Do for each of these words. So we have to specify it now. 166 00:06:00,600 --> 00:06:01,266 And remember we. 167 00:06:01,266 --> 00:06:05,666 Have to specify this before the for loop because that's a single row for loop. 168 00:06:05,966 --> 00:06:06,300 And so. 169 00:06:06,300 --> 00:06:08,233 What we're going to specify is of course. 170 00:06:08,233 --> 00:06:10,600 That we want. To apply. The stemming. 171 00:06:10,600 --> 00:06:13,100 To each of these. Words in the review, which are. 172 00:06:13,100 --> 00:06:14,666 Not just upwards. 173 00:06:14,666 --> 00:06:15,766 And so now the question is. 174 00:06:15,766 --> 00:06:17,700 How do we apply. The stemming. 175 00:06:17,700 --> 00:06:19,733 Well remember we. Just created our. 176 00:06:19,733 --> 00:06:21,666 Border stemmer object. So we're going to start. 177 00:06:21,666 --> 00:06:23,533 With this calling our object. 178 00:06:23,533 --> 00:06:26,366 From which we're going to call specific method. 179 00:06:26,366 --> 00:06:27,433 Which is exactly the. 180 00:06:27,433 --> 00:06:30,433 Stem method. That will apply. The stemming. 181 00:06:30,600 --> 00:06:33,566 And since it is method I'm adding some parentheses. 182 00:06:33,566 --> 00:06:35,366 And now I'm sure you. Guessed perfectly. 183 00:06:35,366 --> 00:06:37,133 What's inside these parentheses. 184 00:06:37,133 --> 00:06:38,600 You know what must be inside. 185 00:06:38,600 --> 00:06:41,033 And that of course our word right. 186 00:06:41,033 --> 00:06:42,566 Our word on which we want to. 187 00:06:42,566 --> 00:06:44,033 Apply. Stemming. 188 00:06:44,033 --> 00:06:45,500 And now there you go, my friends. 189 00:06:45,500 --> 00:06:47,733 All this line of. Code apply. 190 00:06:47,733 --> 00:06:48,800 The stemming to. 191 00:06:48,800 --> 00:06:53,200 All the words in your review except the stopwords, except all. 192 00:06:53,200 --> 00:06:55,133 The non relevant words that are not. 193 00:06:55,133 --> 00:06:57,500 Helpful to predict whether a review is positive. 194 00:06:57,500 --> 00:06:59,466 Or negative. Great. 195 00:06:59,466 --> 00:07:00,733 So that's a good step. Done. 196 00:07:00,733 --> 00:07:01,966 Now you know how. To apply. 197 00:07:01,966 --> 00:07:03,833 Stemming to. A review. 198 00:07:03,833 --> 00:07:04,800 And now since. 199 00:07:04,800 --> 00:07:06,333 We actually have our. 200 00:07:06,333 --> 00:07:08,966 Different words in the review into a. List, we will just. 201 00:07:08,966 --> 00:07:09,766 Join them back. 202 00:07:09,766 --> 00:07:12,666 Together because, you know, we actually had to do. This in order to. 203 00:07:12,666 --> 00:07:15,100 Easily apply. The stemming to all. The words. 204 00:07:15,100 --> 00:07:17,400 Thanks to this for loop, but now that it's done, we will. 205 00:07:17,400 --> 00:07:20,400 Just join these words back together to get the. 206 00:07:20,400 --> 00:07:23,166 Original format of the review, you know, as a string. 207 00:07:23,166 --> 00:07:23,833 And therefore the. 208 00:07:23,833 --> 00:07:25,666 Way to do this is to. 209 00:07:25,666 --> 00:07:27,000 Well, you know, update. 210 00:07:27,000 --> 00:07:28,800 Our review variable. Again. 211 00:07:28,800 --> 00:07:31,166 By setting it equal to, well. 212 00:07:31,166 --> 00:07:31,900 The junction. 213 00:07:31,900 --> 00:07:34,466 Of these different words after we applied. 214 00:07:34,466 --> 00:07:35,466 Stemming to them. 215 00:07:35,466 --> 00:07:36,466 And the way to. Do this is. 216 00:07:36,466 --> 00:07:41,033 By calling the join function, which takes as input. 217 00:07:41,033 --> 00:07:43,800 Exactly. Well our list. You know, the. Review. 218 00:07:43,800 --> 00:07:47,233 Which contains a list of the different words after stemming was applied to them. 219 00:07:47,700 --> 00:07:49,866 And however, if we. Join them, this. 220 00:07:49,866 --> 00:07:51,366 Way, this. Will, you know. 221 00:07:51,366 --> 00:07:53,000 Stick the words together. 222 00:07:53,000 --> 00:07:55,666 And we will end up with one word that makes no sense. 223 00:07:55,666 --> 00:07:56,333 So in order. 224 00:07:56,333 --> 00:07:59,033 To separate these words by a space. 225 00:07:59,033 --> 00:08:03,566 You know, as in a classic string or text, well, we just need to add here in quotes. 226 00:08:03,833 --> 00:08:06,200 And then inside the quotes we add. A. Space. 227 00:08:06,200 --> 00:08:07,900 And then separating this. 228 00:08:07,900 --> 00:08:10,500 With the join with you by a dot. 229 00:08:10,500 --> 00:08:13,833 All right. So this will. Not only join your. 230 00:08:13,833 --> 00:08:18,000 Review but also adding space between each word of your review. 231 00:08:18,333 --> 00:08:19,266 All right. 232 00:08:19,266 --> 00:08:21,466 So good. Now you know how to. Do this as well. 233 00:08:21,466 --> 00:08:24,233 And finally we have. A final step here. 234 00:08:24,233 --> 00:08:26,066 I'm sure you would guess what it is. Otherwise. 235 00:08:26,066 --> 00:08:27,600 Please press pause to just. 236 00:08:27,600 --> 00:08:29,866 Finish. This. Cell implementation. 237 00:08:29,866 --> 00:08:31,200 But that's pretty obvious. 238 00:08:31,200 --> 00:08:32,366 All we did here. 239 00:08:32,366 --> 00:08:35,300 Was a transformation on a single review. 240 00:08:35,300 --> 00:08:36,400 And thanks to this for loop. 241 00:08:36,400 --> 00:08:36,966 Now all. 242 00:08:36,966 --> 00:08:40,133 The different reviews of our data set will. 243 00:08:40,166 --> 00:08:42,566 Be transformed and will be cleaned and essentially will. 244 00:08:42,566 --> 00:08:43,666 Be. Simplified. 245 00:08:43,666 --> 00:08:44,966 But now remember. 246 00:08:44,966 --> 00:08:46,533 We have this corpus here that. 247 00:08:46,533 --> 00:08:48,466 Was initialized as an empty list. 248 00:08:48,466 --> 00:08:49,933 And we want to add. All. 249 00:08:49,933 --> 00:08:51,433 These cleaned reviews into. 250 00:08:51,433 --> 00:08:54,433 The corpus, because that's what will be expected. 251 00:08:54,500 --> 00:08:55,400 By the method of the. 252 00:08:55,400 --> 00:08:57,433 Class that will create the. 253 00:08:57,433 --> 00:08:58,733 Bag of words model. 254 00:08:58,733 --> 00:09:01,000 And therefore we must absolutely now. 255 00:09:01,000 --> 00:09:03,200 Add each review to the corpus. 256 00:09:03,200 --> 00:09:03,533 And the. 257 00:09:03,533 --> 00:09:06,766 Way to do this is by taking our corpus list. 258 00:09:06,966 --> 00:09:07,833 And then. 259 00:09:07,833 --> 00:09:11,200 Call. The append function to append. 260 00:09:11,200 --> 00:09:14,200 Indeed the review. And that's it my friends. 261 00:09:14,366 --> 00:09:16,966 We are done with the cleaning of the texts. 262 00:09:16,966 --> 00:09:18,033 So now we're going to. 263 00:09:18,033 --> 00:09:19,866 Execute. This. You know I think we deserve it. 264 00:09:19,866 --> 00:09:22,366 That was quite a. Long cell implementation. 265 00:09:22,366 --> 00:09:23,100 So let's do this. 266 00:09:23,100 --> 00:09:24,300 However, my run time. 267 00:09:24,300 --> 00:09:27,133 Actually stopped so I have to restart it. 268 00:09:27,133 --> 00:09:29,233 By clicking, you know, runtime and then. Restart. 269 00:09:29,233 --> 00:09:32,400 Runtime. I will execute the two first cells. 270 00:09:32,400 --> 00:09:33,166 And then this one. 271 00:09:33,166 --> 00:09:36,100 And we'll make sure that everything works correctly. 272 00:09:36,100 --> 00:09:37,000 All right. 273 00:09:37,000 --> 00:09:37,400 All good. 274 00:09:37,400 --> 00:09:38,766 Here let's do this. 275 00:09:38,766 --> 00:09:41,133 Let's first re-import the. Libraries. 276 00:09:41,133 --> 00:09:42,966 Done. Import the. Data set. 277 00:09:44,533 --> 00:09:45,300 Done as well. 278 00:09:45,300 --> 00:09:46,733 And now let's see. 279 00:09:46,733 --> 00:09:49,333 And let's hope we. Didn't make any mistake. 280 00:09:49,333 --> 00:09:51,466 Let's play the cell. Cleaning. 281 00:09:51,466 --> 00:09:53,633 All the texts, all the reviews and. 282 00:09:53,633 --> 00:09:55,466 It seems to be all good. And yes. 283 00:09:55,466 --> 00:09:58,033 This is what we were supposed to get. Perfect. 284 00:09:58,033 --> 00:10:00,066 So here it downloaded the, you know. 285 00:10:00,066 --> 00:10:02,200 Stopwords package from this. 286 00:10:02,200 --> 00:10:04,666 Path containing all the Nltk data. 287 00:10:04,666 --> 00:10:06,500 And also it's unzipped. 288 00:10:06,500 --> 00:10:08,200 This zip file. Containing. 289 00:10:08,200 --> 00:10:09,700 All the stopwords and perfect. 290 00:10:09,700 --> 00:10:12,233 And then of. Course, it applied all. This cleaning process. 291 00:10:12,233 --> 00:10:13,500 To each of the reviews. 292 00:10:13,500 --> 00:10:14,666 And adding them to. 293 00:10:14,666 --> 00:10:16,300 The final corpus. Perfect. 294 00:10:16,300 --> 00:10:19,466 So now now it is time for the next essential step. 295 00:10:19,633 --> 00:10:20,800 When doing sentiment. 296 00:10:20,800 --> 00:10:21,600 Analysis, 297 00:10:21,600 --> 00:10:25,633 it will be to create the bag of words model, which will consist basically of. 298 00:10:25,633 --> 00:10:28,600 Creating this sparse matrix containing now all. 299 00:10:28,600 --> 00:10:31,266 The words of the reviews after they. Were cleaned. 300 00:10:31,266 --> 00:10:33,900 So we will take all the different words of all the reviews. 301 00:10:33,900 --> 00:10:37,533 We will put them in different columns of the sparse matrix, and then that will. 302 00:10:37,533 --> 00:10:40,700 Be actually our future matrix of features, which. 303 00:10:40,700 --> 00:10:44,700 We will combine to the dependent variable vector containing all the binary outcomes 304 00:10:44,700 --> 00:10:48,466 to train our future machine learning model, which will be the Naive Bayes 305 00:10:48,466 --> 00:10:51,500 model, to learn the text and understand whether the. 306 00:10:51,500 --> 00:10:53,966 Reviews are positive or negative. 307 00:10:53,966 --> 00:10:56,466 So I can't wait to proceed to this next step. 308 00:10:56,466 --> 00:10:57,000 Creating the. 309 00:10:57,000 --> 00:10:57,966 Bag of Words model with. 310 00:10:57,966 --> 00:11:00,466 You and until then, enjoy machine learning.