Learning from examples is a type of machine learning in which a computer program learns to recognize patterns in data by studying examples of those patterns. There are three main forms of learning from examples:
Supervised Learning: In supervised learning, the computer program is given a set of input-output pairs and is trained to learn a mapping from the input to the output. The program can then use this learned mapping to predict the output for new inputs that it has not seen before.
Unsupervised Learning: In unsupervised learning, the computer program is given a set of input data without any corresponding output data. The program must then learn patterns in the data without being given any guidance as to what those patterns might be.
Reinforcement Learning: In reinforcement learning, the computer program is given a task to perform and must learn through trial and error how to perform the task effectively. The program receives feedback in the form of rewards or punishments based on how well it performs the task, and uses this feedback to adjust its behavior.
Each of these forms of learning from examples has its own strengths and weaknesses, and the choice of which form to use depends on the specific problem being solved and the available data.
