Concept

Binary classification

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Related lectures (32)

Supervised Learning: Image Space and Labeling

Covers supervised learning, binary and multi-class classification problems, and making predictions from labeled examples.

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Explains the strategies for multi-class classification and the concept of decision boundaries.

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Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

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Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

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Covers the parameterization of lines in 2D space and the perceptron algorithm.

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Explores binary classification by regression, decision functions, and various cost functions.

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