Probability and Distributions for Machine Learning

Machine learning is built on the ability to quantify uncertainty and predict outcomes based on data. Understanding probability is the first step in moving from simply running code to truly understanding how models work. This course takes you from the core definitions of chance to the complex distributions that power modern artificial intelligence, providing the mathematical intuition needed for data science. What you'll learn: - Define foundational probability concepts like independent events and conditional logic - Apply Bayes' Theorem to update model beliefs based on new evidence - Analyze discrete and continuous distributions including Binomial, Poisson, and Normal - Understand Marginal and Joint probability in the context of multi-variable datasets - Practice distribution calculations using Python logic and code snippets - Evaluate model uncertainty and its role in modern predictive analytics The course begins with essential terminology and basic laws before progressing to specific distribution types and their practical applications in machine learning algorithms. You will read through detailed explanations and apply your knowledge through written exercises and code-based examples designed to reinforce your understanding of statistical theory. This course is designed for beginners with no prior background in statistics who want to understand the math behind the models. No previous experience with machine learning or advanced calculus is required. Start building your mathematical foundation for machine learning today.