Probability for Machine Learning by Jon Krohn

About this course

Strong machine learning isn’t only about picking the right model or tuning hyperparameters. It’s about making reliable decisions under uncertainty: What could happen, how likely it is, and how confident you should be in what the data suggests. This free online course builds the probability foundations that sit beneath modern AI and machine learning, helping you reason clearly about randomness, variability, and outcomes you can’t predict with certainty.

You will develop a practical grasp of probability as a language for data-driven systems. Instead of treating metrics and model outputs as magic numbers, you’ll learn to interpret them as probabilities connected to events, assumptions, and evidence. The course connects core ideas to the way machine learning actually works, clarifying why probability matters for learning from historical data and for modeling non-deterministic behavior where identical inputs do not always lead to identical outputs.

Through guided explanations and applied exercises, you’ll train your intuition with familiar scenarios like cards and coin tosses. These examples are more than puzzles: they reinforce the mindset you need to understand model evaluation, uncertainty in predictions, and the logic behind learning from multiple observations. You’ll practice thinking in terms of sample spaces and events, and you’ll see how independence changes what you can infer when observations accumulate.

The course also strengthens your combinatorics toolkit, a skill that frequently appears in ML interviews and in day-to-day reasoning about probability distributions. By working through carefully chosen problems, you’ll become faster and more accurate at calculating probabilities for exact outcomes, while also learning how to structure solutions so they scale beyond toy examples. If you want a solid foundation for deeper topics like Bayesian thinking, stochastic optimization, and probabilistic modeling, this course is a clear next step.