# Abstract

The goal of machine learning is to use data to create a predictive model. Machine learning models encapsulate a *function* to calculate an output based on one or more inputs. It creates a model from data.

Consists of two phases:

**Training**— the process of defining the function.**Inferencing**— using the model to predict new values.

# Training Machine Learning Models

Consider an ML model that predicts ice cream sales based on the weather:

- Training data comes from past
*observations*.*Features*($x$) — attributes of the thing being observed (temperature; rainfall; wind speed).- Usually a
*vector*: [$x_1, x_2, x_3, …$]

- Usually a
*Labels*($y$) — values (the number of ice creams sold on a day).

- Algorithms are applied to the training data to find a relationship between the features and the labels and then generalize that relationship as a calculation.
- This is
*fitting*the data to a function.

- This is
- The algorithm’s output ($\hat{y}$) is a
*model*that encapsulates the calculation it performs as a function.