Machine learning (ML) is the science of making computers learn from data without stating any rules. ML is mostly based on models that are trained with a lot of data, such as images of digits or features of different objects, with their corresponding labels, such as the number of those digits or the type of the object. This is called supervised learning. There are other types of learning, such as unsupervised learning and reinforcement learning, but we will be focusing on supervised learning. The main difference between supervised learning and unsupervised learning is that the model learns clusters from the data (depending on how many clusters you specify), which are translated into classes. Reinforcement learning, on the other hand, is concerned with how software agents should take action in an environment in order to increase a reward that is given to the agent, which will be positive if the agent is performing the right actions and negative otherwise.
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