Building upon the probability concepts from the previous chapter, we now shift our focus to how outcomes of random phenomena are organized. Probability distributions provide a structured way to describe the likelihood of different possible results for a random variable. They are fundamental tools for modeling uncertainty and understanding patterns in data, which are common tasks in machine learning.

In this chapter, you will learn to:

• Understand the definition of a probability distribution.
• Distinguish between discrete and continuous distributions.
• Use the Probability Mass Function ($PMF$) to describe discrete outcomes.
• Examine key discrete distributions: Bernoulli and Binomial.
• Use the Probability Density Function ($PDF$) to describe continuous outcomes.
• Examine key continuous distributions: Uniform and Normal (Gaussian).
• Grasp the core idea behind the Central Limit Theorem.
• Generate random samples from these distributions using Python libraries.

By the end of this chapter, you will be familiar with several foundational probability distributions and how to work with them conceptually and programmatically.