From the course: Machine Learning Foundations: Probability
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Probability distributions - Python Tutorial
From the course: Machine Learning Foundations: Probability
Probability distributions
- [Instructor] The easiest way to visualize a probability distribution is to think about it as a relationship that takes random variables and their outcome values and gives the probability of each of these values occurring. For example, if you flip a coin multiple times, we expect that getting heads or tails are equally likely and equal to 1/2. Similarly, if we roll a die 1,000 or 10,000 times, we expect that each number from one to six is equally likely to appear. Random variables can be divided into discrete and continuous. In the case a random variable X is either finite or countably infinite, we call it a discrete random variable, and each distribution is called a discrete probability distribution. It can be represented as a list of all possible outcomes of a random variable X and their probabilities. Let's see a few more examples of a discrete random variable. The number of emergency calls received by a fire…
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