Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn and improve their performance on a given task without explicitly being programmed. These algorithms and models are trained on a dataset, which consists of input data and corresponding labels or outputs. The goal of machine learning is to enable the model to make predictions or decisions based on new, unseen data.
There are different 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, where the correct output is provided for each example in the training set. The model makes predictions based on this input-output mapping. In unsupervised learning, the model is not provided with labeled training examples and must find patterns and relationships in the data on its own. Semi-supervised learning is a combination of supervised and unsupervised learning, where the model is trained on a dataset that is partially labeled. Reinforcement learning is a type of machine learning in which an agent learns to interact with its environment in order to maximize a reward.
Machine learning has many applications, including image and speech recognition, natural language processing, fraud detection, and self-driving cars. It is a rapidly evolving field, with new techniques and technologies being developed all the time.
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