On the other hand, unsupervised learning algorithms
Posted: Mon Feb 03, 2025 4:49 am
Supervised learning refers to algorithms that use labeled training data to help the machine recognize features and apply them to future data. For example, if we want to classify images of cats and dogs then we can feed it some labeled images (indicating whether it is a cat or a dog) and let the machine classify the remaining images.
Are used when the data we have is not labeled, so we simply feed it and let australia consumer email list the machine understand its characteristics and classify them. A common technique to do this is clustering, which basically consists of grouping elements that have similar characteristics.
In reinforcement learning, algorithms interact with the environment by performing actions (experiments) and evaluating whether these actions lead to a reward or punishment. For example, in a game the reward would be winning the game; so the algorithm can play against itself millions of times to analyze the games and learn from the results. In this type of game, the algorithm does not analyze individual moves, but the game as a whole.
Decision trees are a tool that uses a graph to represent all possible decisions for a specific domain and the consequences associated with each decision. They are a very useful way to model an algorithm that only contains conditional control statements (if/else).
Are used when the data we have is not labeled, so we simply feed it and let australia consumer email list the machine understand its characteristics and classify them. A common technique to do this is clustering, which basically consists of grouping elements that have similar characteristics.
In reinforcement learning, algorithms interact with the environment by performing actions (experiments) and evaluating whether these actions lead to a reward or punishment. For example, in a game the reward would be winning the game; so the algorithm can play against itself millions of times to analyze the games and learn from the results. In this type of game, the algorithm does not analyze individual moves, but the game as a whole.
Decision trees are a tool that uses a graph to represent all possible decisions for a specific domain and the consequences associated with each decision. They are a very useful way to model an algorithm that only contains conditional control statements (if/else).