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This is the seventh and final installment of a series of blog posts about the topic of machine learning, how it works and how to actually get started using it. In this blog post I’m going to show you a demo of Kaggle notebooks that I wrote. The demo is built around three different datasets that are of the same type — the problem that we’re trying to solve is to figure out if a given pair of people are friends or not. Of course, this is an oversimplification, but it’s a good place to start, before we dig into some more sophisticated and more complicated datasets. I’m going to use the following Kaggle notebook as a reference when writing these tutorials: This notebook (and all the Kaggle notebooks) is open source, so if you want to learn more about how it works, feel free to play around with it and/or fork it. Tutorial #7: Building a demo from scratch I’m going to start by jumping right into the notebooks because I think it’s a good idea to start with a demo. Here is the notebook I’m going to be using for the demo: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232

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