Artificial intelligence (AI) has an area called "machine learning" that focuses on creating statistical models and algorithms that let computers learn from data without having to be explicitly programmed to do so. The basic concept of machine learning is to use data to gradually enhance a system's functionality or behavior.

Types of Machine Learning:

Supervised Learning: In this method, a model is trained on a labeled dataset that contains inputs and their matching outputs. The model picks up knowledge from existing examples and applies it to predict outcomes for new data.

Unsupervised Learning: Unsupervised learning uses data that has not been labelled. Without explicit instructions, the model's goal is to find latent structures, patterns, or clusters in the data.

Semi-supervised Learning: His hybrid approach includes supervised and unsupervised learning components. It makes use of both labeled and unlabeled data to improve the performance of the model.

Reinforcement Learning: According to this model, an agent interacts with the environment and gets rewarded or punished according to its choices. Over time, the agent learns to maximize rewards, which results in the best possible decision-making.