Conditional Probability in R

In the Probability Fundamentals for R Users course, we covered the fundamentals of probability and learned about:

  • Theoretical and empirical probabilities
  • Probability rules (the addition rule and the multiplication rule)
  • Counting techniques (the rule of product, permutations, and combinations)

In this course, we'll build on what we've learned and develop new techniques that will enable us to better estimate probabilities. Our focus for the entire course will be on learning how to calculate probabilities based on certain conditions — hence the name conditional probability.

By the end of this course, you'll be able to:

  • Assign probabilities to events based on certain conditions by using conditional probability rules.
  • Assign probabilities to events based on whether they are in relationship of statistical independence or not with other events.
  • Assign probabilities to events based on prior knowledge by using Bayes' theorem.
  • Create a spam filter for SMS messages using the multinomial Naive Bayes algorithm.

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Learn Conditional Probability in R

Conditional Probability: Fundamentals

Learn about the fundamentals of conditional probability.

Conditional Probability Continued

Learn more about calculating conditional probabilities.

Bayes' Theorem

Learn about the law of total probability and Bayes' theorem.

The Naive Bayes Algorithm

Learn how to use Naive Bayes to create a spam filter.

Guided Project: Building a Spam Filter with Naive Bayes

Learn to use conditional probability and Naive Bayes in a practical setting.