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Machine learning is a subset of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience, without being explicitly programmed.
Machine learning algorithms use training data to learn patterns and make predictions or decisions without being explicitly programmed. The algorithms are trained using labeled data, and then they use that training to make predictions on new, unseen data.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, unsupervised learning involves finding patterns in unlabeled data, and reinforcement learning involves training a model to make sequences of decisions.
Machine learning is used in a wide range of applications, including image and speech recognition, medical diagnosis, recommendation systems, financial forecasting, and autonomous vehicles.
Some popular machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks. Each algorithm has its own strengths and weaknesses, and is suited to different types of tasks.
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