Python Programming Series- Part 19 (Loss function)

In this video, I explained the theory behind the loss function used in Logistic Regression, known as Logistic loss or Binary crossentropy. We attempted to grasp how to minimize loss values to achieve optimal coefficients and intercepts. I used the Google Colab interface to demonstrate the computation of loss values and the creation of graphs with Predicted labels on the x-axis and Loss values on the y-axis.
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