From the course: Applied Machine Learning: Supervised Learning
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Data: Features, labels, training sets
From the course: Applied Machine Learning: Supervised Learning
Data: Features, labels, training sets
- [Instructor] You'll find in this course that it's actually very easy to make a model. It's like three lines of code. You import the library, you create an instance of a class, and you call fit on it. What usually takes more time is to prepare your data. So let's talk about what kind of data you will need to make a model. In this course, we're going to be working with tabular data, data that is organized in tables. Contrast that with data that is unstructured, such as image data, or audio data, or text data. We're not dealing with that in this course. We're dealing with tabular data, something that you would find in a spreadsheet or a database. There's two main pieces of data that we're concerned with. We're going to call one capital X and the other one lowercase y. X is going to contain your features, and this is organized in a particular way. In X, X is a two-dimensional structure. Each row will represent a sample or an example from your data. So for example, for trying to predict…
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