A probability density function (PDF) is a fundamental concept in statistics and probability theory, particularly for continuous random variables. It describes the likelihood of a random variable taking on a specific value. Unlike discrete probability distributions, where probabilities are assigned to specific outcomes, a PDF provides a density of probabilities across a range of values. The area under the curve of a PDF over a given interval represents the probability that the random variable falls within that interval. Common examples of PDFs include the normal and exponential distributions, which are widely used in various fields, including data science and machine learning.