Al Concepts

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Machine Learning is an application of Artificial Intelligence that gives systems the ability to automatically learn and improve from experience without being explicitly programmed. Instead of writing …

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In conventional programming, the programmer writes the logic and then gives input to get output. In Machine Learning, input data and output data are given to an algorithm, and the algorithm creates th…

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Y = f(X) means that output Y is produced from input X through a learned mapping function f. Here, X stands for input variables, Y stands for output variable, and f is the function learned from labelle…

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It is called supervised learning because the training process is like a teacher guiding the learner. The algorithm learns from labelled training data, where the correct output is already known, and th…

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Supervised learning uses labelled data. Each training example has both the input and the correct output, so the algorithm can learn the mapping and later predict the answer for unseen data.

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Common supervised learning algorithms include Nearest Neighbor, Decision Trees, Linear Regression, and Neural Networks. Different problems may need different algorithms, and no single supervised learn…

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Unsupervised Learning is a machine learning method that works on unlabelled data. The model is not given a specific desired outcome and instead finds patterns, groups, or structure on its own.

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Unsupervised learning is harder because the data has no labels and no clear correct answer is given. The model must discover patterns and structure by itself, so it is less straightforward to design a…