Author(s): Pratik Shukla, Roberto Iriondo

In this series of tutorials, we will dive into probability distributions in detail. We will not just showcase formulas, but instead, we will see how each of the formulas derive from their basic definitions (as it is essential to understand the math behind the derivations), and we will showcase such by using some examples in Python.

This tutorial’s code is available on Github and its full implementation as well on Google Colab.

Table of Contents:

1. What is a Random Variable?
2. Discrete Random Variable.
3. Continuous Random Variable.
4. Probability Distributions.
5. Bernoulli Distribution.
6. Probability Mass Function (PMF).
7. Mean of Bernoulli Distribution.
8. The variance of a Bernoulli Distribution.
9. Standard Deviation of Bernoulli Distribution.
10. Mean Deviation of Bernoulli Distribution.
11. Moment Generating Function for a Bernoulli Distribution.
12. Cumulative Density Function (CDF) for a Bernoulli Distribution.
13. Python Implementation.
14. Summary of the Bernoulli Distribution.
15. Resources.
16. References.

#probability #programming #machine-learning #data-science #python