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TRANSCRIPT
In this video, I want to introduce you guys to a little bit about probability theory and how to compute it and so that we can get sort of using it in many of our applications that we're gonna be working on, so what we're gonna talk about, let's go through, gonna introduce probability just to make sure that everyone's on the same page regarding things like notation and how to actually compute it, and then we're gonna quickly move on to conditional probability, and thus on to Bayes theorem. So Bayes theorem depends on having a knowledge of conditional probability and we're gonna have lots of examples with all of this information as well, and then finally, we're gonna culminate in knowing, we're gonna be learning about the naive Bayes classifier and we're gonna use it, apply it to a set of flights to see if we can predict if our flight is going to arrive late based on any number of given factors, so things like the distance between the two airports, what our departure time is, maybe the airline that we are flying, given all this information we're gonna try to see if we can build a naive Bayes classifier that can predict if our flight is going to arrive late so it's a really cool application of all the probability that we're gonna be learning but we have to actually get started learning some of this probability, so I just wanna start off just introducing some concepts in probability and some notation, just so that everyone is on the same page. So probability is a likelihood of some event happening. So I have two examples here, I have one involving a fair coin, one involving a six-sided dice. So if you think about a fair coin, a fair coin has two sides, and so there's an equal chance or equal probability of the coin landing on heads or landing on tails if you flip it, and so here's just some examples, some notation that we might use, so we have the probability that the coin lands on heads is gonna be equal to 0.5 or 1/2, and here's some notation that you might encounter in other places if you see it, so sometimes probability is denoted as lowercase p and the event is gonna be in parentheses, sometimes, it'll, especially with coins, sometimes just shortened to h or t. Sometimes we capital p, sometimes we'll write the full word Prob for probability but this is just some notation. We might see in many place, there is no standardized way of this notation. And speaking of events, so an event to just some kind of action that has a probabilistic outcome, so flipping a coin is an event, because we don't know what the outcome is yet until we flip the coin. And the chance of the coin is 1/2 on each. Again, a dice toss is another example of an event. There is a one in six chance of it landing on any one particular number, so if you roll a fair dice and it lands on three, the probability that it landed on that three is one out of six.
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