Creating a machine learning algorithm ultimately means building a model that outputs correct information, given that we’ve provided input data. Think of this model as a black box.
We feed input, and it delivers an output. For instance, we may want to create a model that predicts the weather tomorrow, given meteorological information for the past few days. The input we’ll feed to the model could be metrics, such as temperature, humidity, and precipitation.
The output we will obtain would be the weather forecast for tomorrow. Training is a central concept in machine learning, as this is the process through which the model learns how to make sense of the input data. Once we have trained our model, we can simply feed it with data and obtain an output.