In supervised learning, the machine uses labeled training data. It is told the correct output and it compares its own output which informs the subsequent steps, adjusting itself along the way.
This process is repeated until the model achieves a desired level of accuracy on the training data and can correctly predict the class label for new instances.
There are numerous supervised learning algorithms and each has benefits and drawbacks. Read more about types of supervised learning models.
In unsupervised learning, the data isn't labeled. The machine must figure out the correct answer without being told and must therefore discover unknown patterns in the data. Algorithms must therefore be formulated such that they can find suitable patterns and structures in the data on their own.
Some popular examples of unsupervised learning include GANs and Autoencoders.
See types of Unsupervised Learning models.
In Semi-Supervised Learning: Input data is a mixture of labeled and unlabeled examples.
Reinforcement Learning allows the machine the most freedom. It uses trial and error to discover the actions that yield the greatest rewards. AlphaGo is a famous example of RL.