## Course overview

In this course, we’ll build on the fundamentals of probabilities, including the theoretical and empirical probabilities, the probability rules ( the addition rule and the multiplication rule), and the counting techniques (the rule of product, permutations, and combinations).

You’ll learn to assign probabilities to events based on certain conditions by using conditional probability rules, how to assign probabilities to events based on whether they are in a relationship of statistical independence with other events, and how to assign probabilities to events based on prior knowledge by using Bayes’s theorem. You’ll also learn to create a spam filter for SMS messages using the multinomial Naive Bayes algorithm.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.

## Key skills

- Assigning probabilities based on conditions
- Assigning probabilities based on prior knowledge
- Assigning probabilities based on event independence
- Creating spam filters using multinomial Naive Bayes

## Course outline

### Conditional Probability in R [5 lessons]

### Conditional Probability: Fundamentals 1h

Lesson Objectives- Define conditional probability
- Assign probabilities based on conditions
- Identify the standard notation for conditional probability

### Conditional Probability: Intermediate 1h

Lesson Objectives- Define the conditional probability rules
- Employ the multiplication rule
- Identify statistical independence between events

### Bayes Theorem 1h

Lesson Objectives- Assign probabilities based on prior knowledge
- Employ Bayes' Theorem
- Employ the law of total probability

### The Naive Bayes Algorithm 1h

Lesson Objectives- Identify how a spam filter works
- Employ the Naive Bayes algorithm
- Define the multinomial Naive Bayes algorithm

### Guided Project: Building a Spam Filter with Naive Bayes 1h

Lesson Objectives- Expand your portfolio with a guided Naive Bayes project
- Add business value using conditional probability and Naive Bayes
- Employ conditional probability concepts in a practical setting

## Projects in this course

### Building a Spam Filter with Naive Bayes

For this project, we’ll step into the role of data scientists to build a spam filter for SMS messages. We’ll apply conditional probability concepts and use the Naive Bayes algorithm in R.

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