Probability for Machine Learning

📄️ Probability Basics

An intuitive introduction to probability theory, sample spaces, events, and the fundamental axioms that govern uncertainty in Machine Learning.

Understanding how the probability of an event changes given the occurrence of another event, and its role in predictive modeling.

A deep dive into Bayes' Theorem: the formula for updating probabilities based on new evidence, and its massive impact on Machine Learning.

Understanding Discrete and Continuous Random Variables, Probability Mass Functions (PMF), and Probability Density Functions (PDF).

A deep dive into Probability Mass Functions (PMF) for discrete data and Probability Density Functions (PDF) for continuous data.

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