In this lesson, you will learn the fundamentals of conditional probability. As the name implies, these are probabilities based on certain conditions. Conditional probability is one technique that we can use to estimate better probabilities. In addition, you will learn the theoretical knowledge required to understand the algorithm underlying a spam filter — the Naive Bayes algorithm.

Throughout this lesson, you will derive two critical formulas for conditional probability. You will also learn several concepts in the lesson: including conditional probability, the cardinality of a set, and more.

Even though there is no dataset in this lesson, you will still have the opportunity to complete exercises as you go through this lesson. By the end of this lesson, you will be able to feel completely confident with the fundamentals of conditional probability and be well-equipped to better estimate probabilities.

As you work with through learning the fundamentals of conditional probability, you’ll get to apply what you’ve learned from within your browser so that there's no need to use your own machine to do the exercises. The R environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next concept.

#### Objectives

#### Lesson Outline

1. Introduction

2. Brief Recap

3. Updating Probabilities with New Information

4. Conditional Probability

5. Conditional Probability Formula

6. Example Walkthrough

7. Conditional Probability Formula Revisited

8. Next steps

9. Takeaways