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Key Machine Learning Terminologies are
Machine learning systems uses Relationships between Inputs to produce Predictions.
In algebra, a relationship is often written as y = ax + b:
With ML, a relationship is written as y = b + wx:
In Machine Learning terminology, the label is the thing we want to predict.
It is like the y in a linear graph:
| Algebra | Machine Learning |
| y = ax + b | y = b + wx |
In Machine Learning terminology, the features are the input.
They are like the x values in a linear graph:
| Algebra | Machine Learning |
| y = ax + b | y = b + wx |
Sometimes there can be many features (input values) with different weights:
y = b + w1x1 + w2x2 + w3x3 + w4x4
A Model defines the relationship between the label (y) and the features (x).
There are three phases in the life of a model:
The goal of training is to create a model that can answer a question. Like what is the expected price for a house?
Inference is when the trained model is used to infer (predict) values using live data. Like putting the model into production.