New to statistical and machine learning and taking some online courses.

I am trying to understand logistic regression in more details, and noticed a difference in the formula between the Andrew Ng course and the Stanford statistical learning course. Below I post a link image to both formulas. So the numerator is different. I must be missing smthg as I am a noob in this:

$$p(X)=\frac{e^{\beta_0+\beta_1X}}{1+e^{\beta_0+\beta_1X}}$$

and $$h_\theta(x)=\frac{1}{1+e^{-\theta^\top x}}.$$

http://prntscr.com/dmyvx7

**Answer**

$h_{\theta}(x)=\dfrac{1}{1+e^{-X\beta}}=\dfrac{1}{1+\frac{1}{e^{X\beta}}}=\dfrac{1}{\dfrac{e^{X\beta}}{e^{X\beta}}+\dfrac{1}{e^{X\beta}}}=\dfrac{1}{\dfrac{e^{X\beta}+1}{e^{X\beta}}}=\dfrac{e^{X\beta}}{1+e^{X\beta}}=p(X)$

**Attribution***Source : Link , Question Author : stats999 , Answer Author : Community*