When dealing with unstructured data, be it text or images, we must first convert the data into a numerical representation that's usable by our machine learning model. The process of converting data that is non-numeric into a numerical representation is called feature extraction. For image data, our features are the pixel values of the image.
First, let's imagine a 1,150 x 1,150 pixel grayscale image. A 1,150 x 1,150 pixel image will return a 1,150 x 1,150 matrix of pixel intensities. For grayscale images, the pixel values can range from 0 to 255, with 0 being a completely black pixel, and 255 being a completely white pixel, and shades of gray in between.
To demonstrate what this looks like in code, let's extract the features from our grayscale cat burrito. The image is available on GitHub at https://github.com/PacktPublishing/Python-Machine...
