What is correlation , regression , gradient descent

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Correlation measures the relationship between two variables, regression predicts a dependent variable based on independent variables, and gradient descent is an optimization algorithm for finding the minimum of a function.

Correlation measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1.
Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It helps in predicting the value of the dependent variable based on the independent variables.
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent. It is commonly used in machine learning for training models.
Example: In a study, correlation can be used to determine if there is a relationship between hours of study and exam scores. Regression can be used to predict a student's exam score based on the number of hours studied. Gradient descent can be used to optimize the parameters of a machine learning model to minimize the error.

What is correlation , regression , gradient descent

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