The subject of likelihood is the subject of probability theory, which is a branch of mathematics. Probability theory is the mathematical foundation of statistical reasoning, and it is critical for data scientists to understand how unpredictability affects data. It is crucial in machine learning since probabilistic data assumptions are regularly used in the construction of learning algorithms. The idea of probability is used to quantify the degree of uncertainty. The purpose of probability theory is to use a set of axioms to express uncertain phenomena. To cut a long story short, we try to describe the situation by estimating the likelihood of various occurrences and scenarios when we cannot be positive of the system's likely outcomes. This article will cover random variables, independence, entropy, and the Chebyshev inequality. These mathematical theorems and notions are necessary and vital for future research in other fields of science.