Let's talk about the **Bayes theorem**!

**What is it?**

The theorem helps figure out the probability that one event will happen, considering that another statistically related one happened.

I strongly believe that everything is clear only with examples, so let's move to an example.

**Example:**

Let's assume that we have a box with red and green balls. There are **600** red balls and **900** green balls. We know that among all balls, **5% of red balls** have a defect and **20% of green balls** have a defect too. Randomly we pulled out a ball with a defect, calculate the probability that it relates to the red ones.

It should be noted that this theorem is widely used in the data science; therefore, look to the general formula:

It's time to dive deeper into the probability theory. Here we will learn basic formulas. The main idea of the section is to help us to understand the term probability using real-life examples. 

Here, we will get acquainted with the rules in probability theory. This section will be more complicated than the previous one. It will be attractive because we are going to work with the real challenges. But it should be noted that we learn some formulas.

Doing experiments is always funny, but here we will combine exciting tasks with programming learning. In this section, we will work with the binom object to conduct experiments in Python.

Distribution is a key to probability learning, but don't be petrified by this term. This section provides you with real-life explanations and relevant formulas. 

The normal distribution is the most commonly used one. Here we will work with the distribution with the help of real-life challenges.

Task