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Update learn.md
not sure it works - but latex is not rendered correctly now for the "where" section - added "$$"
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questions/37_calculate-correlation-matrix/learn.md

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#### Where:
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- \( \text{cov}(X, Y) \) is the covariance between \( X \) and \( Y \).
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- \( \sigma_X \) and \( \sigma_Y \) are the standard deviations of \( X \) and \( Y \), respectively.
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- $$ \text{cov}(X, Y) $$ is the covariance between \( X \) and \( Y \).
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- $$ \sigma_X $$ and $$ \sigma_Y $$ are the standard deviations of \( X \) and \( Y \), respectively.
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### Problem Overview
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In this problem, you will write a function to calculate the correlation matrix for a given dataset. The function will take in a 2D numpy array \( X \) and an optional 2D numpy array \( Y \). If \( Y \) is not provided, the function will calculate the correlation matrix of \( X \) with itself.

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