September 2017
Beginner to intermediate
304 pages
7h 2m
English
Content preview from Machine Learning With Go
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Random variables
Let's say that we have an experiment, like our scenario of flipping the coin, which could have multiple outcomes (heads or tails). Now let's define a variable whose value could be one of these outcomes. This variable is referred to as a random variable.
In the case of the coin toss (at least if we are considering a fair coin), each of the outcomes of the random variable is equally likely. That is, we have a 50% chance of seeing a heads outcome and a 50% chance of seeing a tails outcome. Yet, the various values of the random variable need not be equally likely. If we are trying to predict whether it will rain or not, those outcomes will not be equally likely.
Random variables allow us to define these how likely and how significant ...
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