## Mathematics

The main branches of Mathematics involved in AI are:

- Linear Functions
- Linear Graphics
- Linear Algebra
- Probability
- Statistics

## AI = Mathematics

Behind every AI success there is **Mathematics**.

All AI models are constructed using solutions and ideas from math.

The **purpose** of AI is to create **models** for understanding **thinking**.

If you want an AI career:

- Data Scientist
- Machine Learning Engineer
- Robot Scientist
- Data Analyst
- Natural Language Expert
- Deep Learning Scientist

You should focus on the mathematic concepts described here.

## Linear Functions

- Linear means
**straight** - A
**linear function**is a**straight line** - A
**linear graph**represents a**linear function**

## Graphics

- Graphics plays an important role in
**Math** - Graphics plays an important role in
**Statistics** - Graphics plays an important role in
**Machine Learning**

## Linear Algebra

Linear algebra is the bedrock of data science.

Knowing linear algebra boosts your ability to understand data science algorithms.

Scalar | Vector(s) |

1 | 123 123 |

Matrix | Tensor |

123456 | 123456 456123 |

## Probability

**Probability** is how likely something is to occur, or how likely something is true.

I have 6 balls in a bag: 3 reds, 2 are green, and 1 is blue.

Blindfolded. What is the probability that I pick a green one?

Number of **ways** it can happen are 2 (there are 2 greens).

Number of **outcomes** are 6 (there are 6 balls).

The probability is 2 out of 6: 2/6 = 0.333333…

Probability = Ways / Outcomes

## Statistics

Statistics is about how to collect, analyze, interpret, and present data.

Statistics works with questions like:

- What is the most
**Common?** - What is the most
**Expected?** - What is the most
**Normal?**