Course link:
https://developers.google.com/machine-learning/crash-course/framing/video-lecture
ML systems learn how to combine input to produce useful predictions on never-before-seen data.
是我們想要 predicting 的事物，也可認為是 the y variable in simple linear regression。
我們輸入的變數，可視為 x，以找垃圾郵件為例 (spam detector example)，Features 可以是:
- words in the email text
- sender's address
- time of day the email was sent
- email contains the phrase "one weird trick."
A model defines the relationship between features and label. Training means creating or learning the model. Inference means applying the trained model to unlabeled examples.
model 表現了 features and label 之間的關係，Training 就是在找關係間的學習行為。
A regression model 預測連續的值，像是某地區房價或是某事件發生機率。A classification model 是預測分離(離散)的值，例如判斷某個email是不是spam，或是某張圖片裡的動物是不是狗。