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All language subtitles for 02 - Module Overview.en

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 0 00:00:01,040 --> 00:00:02,140 [Autogenerated] hi and welcome to the 1 00:00:02,140 --> 00:00:04,110 scores on preparing data for feature 2 00:00:04,110 --> 00:00:06,330 engineering on machine learning in this 3 00:00:06,330 --> 00:00:08,589 model will understand the rule off 4 00:00:08,589 --> 00:00:11,810 features in machine learning. We start our 5 00:00:11,810 --> 00:00:14,259 discussion by going over how machine 6 00:00:14,259 --> 00:00:16,940 learning algorithms can learn from. Data 7 00:00:16,940 --> 00:00:18,670 will then discuss the significance and 8 00:00:18,670 --> 00:00:20,960 importance on the role of daytime machine 9 00:00:20,960 --> 00:00:23,480 learning. We'll discuss what features and 10 00:00:23,480 --> 00:00:25,489 labels mean in the context. Off machine 11 00:00:25,489 --> 00:00:28,339 learning features refer to the data that 12 00:00:28,339 --> 00:00:30,620 we used to train the model labels are 13 00:00:30,620 --> 00:00:33,039 predictions from our model build and get a 14 00:00:33,039 --> 00:00:35,149 big picture understanding off the machine 15 00:00:35,149 --> 00:00:37,200 learning workflow and the various 16 00:00:37,200 --> 00:00:39,619 processes that are involved Building. 17 00:00:39,619 --> 00:00:42,189 Focus our attention on the data 18 00:00:42,189 --> 00:00:44,909 preparation stage. This involves feature 19 00:00:44,909 --> 00:00:46,789 engineering to convert date are two 20 00:00:46,789 --> 00:00:48,869 features. We'll see how feature 21 00:00:48,869 --> 00:00:51,479 engineering is neither art nor science. 22 00:00:51,479 --> 00:00:54,250 It's basically just engineering on how it 23 00:00:54,250 --> 00:00:56,439 involves many techniques that fall under 24 00:00:56,439 --> 00:00:58,049 the broad umbrella off featured 25 00:00:58,049 --> 00:01:00,170 engineering, and we'll round off our 26 00:01:00,170 --> 00:01:01,939 discussion the discussion off the 27 00:01:01,939 --> 00:01:04,430 different kinds of data that we work with 28 00:01:04,430 --> 00:01:06,890 when we build and train animal models, 29 00:01:06,890 --> 00:01:10,000 training data, test data and validation data 2293