Core Concepts

Supervised machine learning is based on the following core concepts,

• Data
• Model
• Training
• Evaluating
• Inference

Data

• Data is the driving force of ML. Data comes in the form of words and numbers stored in tables, or as the values of pixels and waveforms captured in images and audio files.
• We store related data in datasets. For example, we might have a dataset of the following:
  • Images of cats
  • Housing prices
  • Weather information

Datasets

Datasets are made up of individual examples that contain features and a label.

• Features : Are the values that supervised model uses to predict a label. Means the variables which are directly affect for the outcome.
• Examples : An example analogous to a single row in a spreadsheet.
• Labels : Is the value we want the model to predict.