Machine learning (ML) is a branch of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn from data and improve their performance on specific tasks without being explicitly programmed for each particular task.

In machine learning, models are trained using training data sets, and then used to make predictions or decisions based on new data. Machine learning models can be supervised or unsupervised, depending on whether or not prior information on the class or label of the training data is provided.

Many of the algorithms developed are based on biological nervous systems, that emulate the biological process of recognition, learning and generalization.

However, Machine Learning is not a simple process. As algorithms ingest training data, it is possible to create more accurate models based on this data. A Machine Learning model is the result that is generated when a Machine Learning algorithm is trained with data. For example, a predictive algorithm creates a predictive model.

After training, when a model is provided with input data, it receives an output result, which allows it to solve the problem for which it was trained.

Machine learning models are used in a wide variety of applications, including pattern recognition, natural language processing, computer vision, fraud detection, process optimization and decision making. Machine learning is a key technology in the development of artificial intelligence systems and autonomous robots.