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.

The Program

1. Conditional Probability: Fundamentals.
2. Conditional Probability: Intermediate.
3. Bayes Theorem.
4. The Naive Bayes Algorithm.
5. Guided Project: Building a Spam Filter with Naive Bayes.

What will you learn

• How to assign probabilities based on conditions.
• How to assign probabilities based on prior knowledge.
• How to assign probabilities based on event independence.
• How to create spam filters using multinomial Naive Bayes.