Gooey confusion

Logistic Regression It estimates the probability of an outcome (aka dependent variable) based on a set of one or more input (aka independent variables or "features") Can be used for binary classification problem (only 2 possible outcomes) or multiclass classification problem (more than 2 outcomes) For example, it can be used to predict the chances of a particular transaction is fraud given some input information like the customer's details, transaction details etc.  If there are more than 2 outcomes like the EPL game (i.e. win, draw and lose), logistic regression also can be used as it can determine the possibilities of each outcome.  Logistic Regression Function is defined as follows: Logistic Regression Function Note: f(x) represents the probability of an output y  given each input x .  From the formula above, it tells us that it can take in any input x (positive or negative) and produce an output y in the range of 0.0 to 1.0.  Back to...

Well...for the past weeks, we have been rushing to complete the Andrew Ng's Deep Learning module so that we can complete it in a month.  About Andrew's Ng Deep Learning @ Coursera: For those who do not know, there are 5 courses for this Deep Learning module and each course will take about a few weeks to complete. You will have to input your credit card details before you can access the 7-days free trial for this module. Pretty smart way to lock in customers uhhh...Anyway, after the free trial period, if you wish to continue, you have to pay USD 49 per month. Otherwise, REMEMBER to 'un-enroll' the course.   https://www.coursera.org/specializations/deep-learning Confessions To be honest, we have only completed 99% of the module as  we didn't do the last programming assignment  😳. Why didn't we continue? Initially, we were very motivated to finish the whole module since we have already prepaid for the first month and we wanted to setup our AI busi...