Supervised Learning and Labeled Data
Definition: In supervised learning, the goal is to learn a mapping between input data and output
labels, where the output labels are provided in a training dataset.
Labeled Data: In labeled data, each example is associated with a label that corresponds to the
desired output.
Examples: Image classification, spam detection, and speech recognition.
Process:
Collect and preprocess data.
Definition: In supervised learning, the goal is to learn a mapping between input data and output
labels, where the output labels are provided in a training dataset.
Labeled Data: In labeled data, each example is associated with a label that corresponds to the
desired output.
Examples: Image classification, spam detection, and speech recognition.
Process:
Collect and preprocess data.
