Logistic Regression

How to implement a logistic regression classifier using gradient ascent in the simple case where there is only one feature variable x1:

Normalize the feature variable x1 (Can Provide details on it).
• Calculate the value of the objective function l(β0,β1).
• Choose a value of the learning rate η (Can we try different values?).
• Initialize the parameter value and calculate the gradient ∇l(β0,β1).
• Update the parameter value.
• Check whether gradient ascent has converged.
(Here, We can rather look at the convergence of the values of l(β0,β1) than the convergence of the parameters themselves.)
• Complete the implementation of gradient ascent.

Looks like you got some good help on stackoverflow already