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Insert one 'tab' between the speaker name and the spoken dialogue as you transcribe the recording. Insert a line break between each paragraph as you transcribe the recording. Bold each speaker label as you transcribe the recording.
Data labeling, in the context of machine learning, is the process of detecting and tagging data samples. The process can be manual but is usually performed or assisted by software.
Labeled data is a group of samples that have been tagged with one or more labels. After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data.
Data labeling is the manual curation of data by humans on machine learning and AI applications. Roughly we call it supervised machine learning because computers need human supervision to get trained to execute tasks that are tricky for machines, but definitely easy for humans such as image recognition.
To classify something is to label it, they are the necessarily same thing. Label is much simpler, and in all cases, classification is just the act of putting labels on objects (or learning to correctly do so).
We can say that labeled is that data which is well-defined. E.g. Emails, IP addresses, etc. Whereas unlabeled data is something which is not properly defined.
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of that unlabeled data with meaningful tags that are informative.
Labeled data, used by Supervised learning to add meaningful tags or labels or class to the observations (or rows). These tags can come from observations or asking people or specialists about the data. Classification and Regression could be applied to labelled datasets for Supervised learning.
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