 Data can come in all shapes and sizes. It can be a simple spreadsheet or a complex text, audio, video or even an image. Today I will show you how Orange transforms images into numbers and in this way enables machine learning. First, we will have to install Image Analytics add-on. Go to Options, Add-ons and install Image Analytics. Restart Orange for the add-on to appear. Now let's start with an example. I have collected 19 images of domestic animals and placed them all in the same folder. I can load them in Orange with import images. Open the widget and select the folder. Now connect Image Viewer to import images. Looks like we have all the animals here. Import images actually constructs and outputs a data table. But how does this data table look like? Let's check this in a data table widget. All I have is a bunch of meta information. Image name, image file path, size of the file, image width and height in pixels. Nothing that would help me with machine learning. We need image descriptors. That is numbers which describe the content of these images. We will transform raw images into their vector representation using a deep neural network which was trained on millions of real-life images. Retreat vectors are also called image embeddings. We embed our images into a multi-dimensional feature space. Connect import images to image embeddings. The widget sends images to the server and computes embeddings remotely. Let's see what we got back. Now we don't only have meta information but also 2048 additional features to profile the images. Great! With embeddings we can compare the images and compute their similarity. Let us pass the data to distances and select cosine distance as it usually works best for images. Then we can pass the distance matrix to hierarchical clustering and observe the dendrogram. Seems like embeddings indeed make sense. All the chickens are grouped together and all the cows too. I can even check each cluster in an image viewer. Simply wonderful! But I know what you're going to say. You think I'm trying to trick you that I intentionally selected similar photos so they would get clustered together. Let's double check our procedure with the most famous cow in Europe, the Milka cow. This image is quite different from the others. It is a frontal photograph with digitally enhanced colors. I have retrieved the image from the internet and added it to my domestic animals folder. If I press reload in import images, the new image gets loaded and the whole workflow is instantly updated. Let us again open hierarchical clustering and image viewer. Seems like Milka is correctly clustered with its kind. Today we've learned how to work with images in orange, profile them using deep model embeddings and perform clustering. In the next video we will go a step further. We will be building predictive models for image classification.