In this first mission of our probability fundamentals course, you will learn about what probability is and how it's used in real-world data science work. Estimating probabilities is an extremely powerful technique that can enable us to build non-trivial applications, including Image recognition systems, spam filters, statistical hypothesis tests, and much more! If you want to learn machine learning, for example, understanding probability is key.
Later in the mission, you'll learn about random experiments, the term given to any process for which we cannot predict outcomes with certainty. Even though we cannot predict these outcomes with certainty, you'll learn how to estimate the empirical probability and theoretical probability of a certain outcome.
As you explore each concept in this mission, you will get the opportunity to practice and apply your knowledge using your Python coding skills. You'll also learn how to simulate a coin toss or a roll of a dice to estimate probability without running your Python script a predetermined amount of times!
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