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# Fundamentals of Probability for Machine Learning

5 Chapters 19 Lessons Intermediate

In this course, the probability theory is described.Â

The probability theory is of great importance in many different branches of science. Let's focus on Artificial Intelligence empowered byÂ Machine Learning. The question is,Â "how knowing probability is going to help us in Artificial Intelligence?"Â In AI applications, we aim to design an intelligent machine to do the task.Â

First, the model shouldÂ get a sense of the environmentÂ via modeling. As there is ambiguity regarding the possible outcomes, the model works based on estimation and approximation, which are done via probability.Â

Second, as the machine tries to learn from the data (environment), it must reason about the process of learning and decision making. Such reasoning is not possible without considering all possible states, scenarios, and their likelihood.Â

Third, to measure and assess the machine capabilities, we must utilize probability theory as well.

Here, you will learn what is necessary for Machine Learning from probability theory.

Course Objectives:

• Learn the core concepts of probability theory.
• Becoming familiar with mostly used probability concepts and distributions in Machine Learning
• Understand the mathematical foundation of probability theory and their applications

# Course Structure

## Fundamentals 6 Lessons

### Independence

LIMITED FOR NON-SUBSCRIBERS

## Discrete Random Variables 4 Lessons

### Some Discrete Probability Distributions

LIMITED FOR NON-SUBSCRIBERS

## Continuous Random Variables 4 Lessons

### Some Continuous Probability Distributions

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