# Probability for Machine Learning

Probability refresher for machine learning.
Free tutorial
Rating: 4.1 out of 5 (14 ratings)
1,603 students
52min of on-demand video
English
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Refresh probability fundamentals.
Use conditional probability and Bayes' rule in machine learning
Use random variables in machine learning
Use probability distribution functions in machine learning
Manipulate multiple and interdependent random variables
Understand law of large numbers
Recall the most frequently used probability distribution functions in machine learning

## Requirements

• Basic knowledge of probability
• Basic real analysis (integration)

## Description

Probability is usually a prerequisite of machine learning. However, one doesn't need to know all the concepts in probability.

In this course, I have compiled together all the important probability concepts that are most frequently used in machine learning. This is the content I taught at Polytechnique Montreal as a refresher on probability for machine learning. Understanding these concepts will help you navigate through an introductory course in machine learning.

This course is for you if

- You have learned probability a long time ago

- You want to refresh the essential topics in probability to get started with your journey in machine learning.

This course is not for you if

- You want to learn probability from scratch.

- You want to master all the concepts in probability.

Please note that I do not cover all the topics in probability. I only cover the topics that are most frequently used in the machine learning textbook. If you want to learn probability from scratch or master all the concepts, this course is not for you.

In this course, we cover the following topics

Probability basics

Conditional probability and Bayes’ rule

Random variables

Expectation and Variance

Multiple random variables

Law of large numbers

Some important distribution functions

## Who this course is for:

• Students who want to start their journey in machine learning and want to refresh the probability topics needed for that.

## Instructor

Ex Googler and PhD Student at Polytechnique Montreal
• 4.2 Instructor Rating
• 133 Reviews
• 16,841 Students
• 3 Courses

I graduated from BITS Pilani Goa Campus in 2015 with B. E. (Hons.) Computer Science and M. Sc. (Hons.) Mathematics. After graduation, I worked with Google from 2015 to 2020 with operations research team. With the operations research team I worked as a developer of Google OR-Tools. I Left Google in 2020 to start my PhD under the supervision of Prof. Andrea Lodi and Prof. Guy Desaulniers in applied mathematics.

My research interests:

Discrete optimization

Column Generation

Machine Learning.