Probability Theory for AI and Machine Learning

Behind every successful machine learning algorithm lies a foundation of probability theory that guides how models learn, make predictions, and handle uncertainty. This text-based course bridges the gap between abstract mathematical formulas and practical artificial intelligence concepts, giving you the clarity needed to excel in data science. You will transition from understanding basic randomness to confidently implementing probabilistic reasoning in modern AI workflows. What you'll learn: - Understand foundational concepts of probability, sample spaces, and random variables - Apply Bayes' theorem to update beliefs and manage uncertainty in predictive models - Master core probability distributions including Gaussian, Binomial, and Poisson distributions - Practice parameter estimation techniques such as Maximum Likelihood Estimation - Analyze how probability theory directly powers modern machine learning algorithms and neural networks - Explore modern probabilistic programming concepts and Bayesian network structures The course begins with vital terminology and core definitions of probability before guiding you step-by-step through statistical distributions, parameter estimation, and real-world AI applications. Written explanations are paired with clear code snippets to ensure you can translate mathematical theory into working software. Designed specifically for beginners, this course requires only basic math knowledge and introductory programming familiarity. Start your journey toward mastering the mathematical core of artificial intelligence today.