In Chapter 1, Introduction to Machine Learning, we saw many different types of learning algorithms and learned that ML is really a process of automatically discovering rules given a set of examples. The main components required for this process, specifically for supervised learning, include: 

• Input data points: For image classification, we would require images of the domain we want to classify, for example, animals. 
• The expected outputs for these inputs: Continuing from our previous example of image classification of animals, the expected outputs could be labels associated with each of the images, for example, cat, dog, and many more. 
• A ML algorithm: This is the algorithm used to automatically learn how to transform the input data points into a meaningful output. These derived sets of rules are what we call the model, derived through a process of learning called training. 

Let's make these concepts more concrete by working through a simple example.