Machine Learning — Flashcards

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Machine Learning is a subset of Artificial Intelligence in which systems learn from data to improve performance on a task without being explicitly programmed. It uses algorithms to find patterns, make…

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Arthur Samuel coined the term Machine Learning in 1959 while at IBM. He defined it as the field of study that gives computers the ability to learn without being explicitly programmed.

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It means the model learns patterns and correlations from examples in the data. As it sees more data, its predictions and decisions usually become more accurate and more useful for unseen cases.

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The key types are supervised learning, unsupervised learning, reinforcement learning, and generative AI. These differ in the kind of data they use and the way they learn patterns or generate outputs.

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Supervised learning is trained on labeled data, where each input has a known correct output. A spam filter is an example because it learns from emails already labeled as spam or not spam.

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Unsupervised learning works on unlabeled data and finds patterns without a pre-defined output. Clustering is a common example, and it is used in customer segmentation.

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Reinforcement learning is a type of machine learning in which an agent learns through trial and error by receiving rewards for correct actions and penalties for incorrect actions. It is used to train …

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Generative AI creates new content such as text, images, or music by learning patterns from existing data and mimicking them.