[{
"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples/Code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
Framing
This module investigates how to frame a task as a machine learning problem, and
covers many of the basic vocabulary terms shared across a wide range of machine
learning (ML) methods.
Framing
What is (Supervised) Machine Learning?
ML systems learn
how to combine input
to produce useful predictions
on never-before-seen data
Terminology: Labels and Features
Label is the variable we're predicting
Typically represented by the variable y
Terminology: Labels and Features
Label is the variable we're predicting
Typically represented by the variable y
Features are input variables describing our data
Typically represented by the variables {x1, x2, ..., xn}