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, to assign probabilities to events based on whether they are in a relationship of statistical independence or not with other events, and 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.
- Assigning probabilities based on conditions
- Assigning probabilities based on prior knowledge
- Assigning probabilities based on event independence
- Creating spam filters using multinomial Naive Bayes
Introduction to Conditional Probability in Python [5 lessons]
Conditional Probability: Fundamentals 1hLesson Objectives
- Define conditional probability
- Assign probabilities based on conditions
- Employ standard notation for conditional probability
Conditional Probability: Intermediate 2hLesson Objectives
- Define conditional probability rules
- Employ the multiplication rule
- Identify statistical independence between events
Bayes Theorem 1hLesson Objectives
- Assign probabilities based on prior knowledge
- Employ Bayes' Theorem
- Employ the law of total probability
The Naive Bayes Algorithm 2hLesson Objectives
- Explain 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 2hLesson Objectives
- Create a spam filter using multinomial Naive Bayes
- Expand your portfolio using conditional probability and Naive Bayes
- Employ conditional probability concepts
Projects in this course
Guided Project: Building a Spam Filter with Naive Bayes
Learn to use conditional probability and Naive Bayes in a practical setting.
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