Machine learning is a subfield of artificial intelligence focused on the development and use of algorithms and statistical models enabling computers to learn from data and improve their performance on a specific task. In machine learning, a computer is fed a large dataset and uses that data to train a model to perform a specific task. The model is then tested on a separate dataset to evaluate its performance. If the model performs well, it can be deployed in a real-world application. If the model does not perform well, it can be adjusted and retrained using additional data or different algorithms until it performs satisfactorily. There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. • In supervised learning, the model is trained on a labeled dataset, meaning that the input data accompanies the correct output. The model makes predictions based on this labeled data and is then tested on a separate dataset to evaluate its accuracy. • In unsupervised learning, the model is not provided with labeled data and must find patterns in the input data on its own. • In semi-supervised learning, the model is provided with some labeled data and some unlabeled data and must use the labeled data to make predictions about the unlabeled data. • In reinforcement learning, the model is trained to take actions in an environment to maximize a reward.
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