Many machine learning techniques are built upon probabilistic reasoning. This chapter revisits the essential concepts of probability theory to ensure a solid foundation for the statistical methods covered later. We begin with the definitions of sample spaces and events, followed by conditional probability ($P(A|B)$) and independence. You will learn about Bayes' Theorem, a key tool for updating probabilities based on new evidence: $P(B|A) = \frac{P(A|B)P(B)}{P(A)}$ The chapter introduces random variables, covering both discrete and continuous types, along with methods to compute their expected value ($E[X]$) and variance ($Var(X)$). Lastly, we will demonstrate how to implement these fundamental probability concepts using Python.