In the first lesson of our probability fundamentals course, you’ll learn what probability is and how it’s used in the real world.

Probability estimation is an important foundational technique that allows us to build complex applications like image recognition systems, spam filters, statistical hypothesis tests, and much more. And if you’re interested in machine learning, you’ll need a good basis in probability.

We’ll also discuss random experiments, the term given to any process for which we cannot predict outcomes with certainty. Even though we can’t predict their outcomes with certainty, you'll learn how to estimate the empirical probability and theoretical probability of a certain outcome.

In this lesson, you’ll practice each probability concept in theory, and then put it into practice by translating that concept into code. You'll also learn how to simulate a coin toss or dice roll to estimate probability without re-running your Python script! And with our built-in answer checker, you can ensure that you have mastered the fundamentals of probability.


  • Learn about random experiments
  • Learn how to estimate theoretical probabilities
  • Learn how to estimate empirical probabilities

Lesson Outline

1. Introduction
2. The Emprical Probability
3. Probability as Relative Frequency
4. Repeating an Experiment
5. The True Probability Value
6. The Theoretical Probability
7. Events vs. Outcomes
8. A Biased Die
9. Next Steps
10. Takeaways