works by using algorithms and statistical models to enable computers to Lear from data and make predictions or decisions without explicit instructions. There are several different approaches to AI including
Supervised learning: This involves training model on a labeled dataset, where the desired output or label is already known. The model can then be used to make predictions on new unseen data.
Unsupervised learning: This involves training a model on an unlabeled dataset where desired output is not known. The model must find patterns or structure in the data.
Reinforcement learning: This involves training an agent to make decisions in an environment, where it receives feedback in the form of rewards or punishments. The agent learns to optimize its decision-making over time to maximize the cumulative reward.
Deep learning: This is a subfield of machine learning that involves the use of neural networks, which are composed of layers of interconnected recognize patterns in data.
Generative models: These models are trained on a dataset and learn the underlying probability distribution of the data Once trained they can generate new data that is similar to the original data.
Overall, the primary goal of AI is to enable computers to perform tasks that would normally require human intelligence, such as recognizing patterns, understanding natural language, and making decisions. To achieve this, AI systems are trained on large amounts of data, and use algorithms and statistical models to extract useful information and make predictions or decisions.
