This notebook translates the symbols you see most often in machine learning into plain English and business meaning.## Symbol Dictionary| Symbol | Name | Meaning | Business translation || --- | --- | --- | --- || $\mu$ | mean | average value | average order size || $\sigma$ | standard deviation | typical spread | KPI volatility || $\sigma^2$ | variance | squared spread | instability or risk || $\Sigma$ | summation | add a sequence of terms | total portfolio value || $\beta$ | coefficient | learned feature weight | impact of one driver || $\theta$ | parameter | model setting | what the model learns || $\lambda$ | penalty term | regularization strength | how strongly complexity is penalized || $P(A \mid B)$ | conditional probability | chance of A given B | chance of purchase given visit |## How to Read a Formulamermaidflowchart TD A[Symbol] --> B[Name] --> C[Math meaning] --> D[Business meaning]Alt text: Each symbol should be decoded from appearance to name, then math meaning, then business meaning.## Worked Example

This means:- $y$ is the predicted output- $\beta_0$ is the baseline- $\beta_1$ is the effect of the input- $x$ is the input feature- $\epsilon$ is leftover noise## Quick Check1. Which symbol usually means average?2. Which symbol often means a coefficient?3. Which notation means one event conditioned on another?