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What are the two approaches to reducing the size of datasets for training?
What steps are part of cleaning & enhancing data? Select all that apply.
What approach can be used to protect personally identifiable information (PII) in training data?
Data Labeling is considered the Achilles Heel of AI.
What are some approaches to Data Labeling (select all correct answers)
Match the Data Preparation term to the definition [drag the term to the definition]
Techniques used to enhance existing data through the use of additional data, manipulations on existing data, or combinations of data in various ways
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Necessary additional metadata that is applied to training data to provide the meaning necessary to train supervised learning machine learning models
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Boundary lines drawn around an object or set of objects to serve as a point of reference for object detection
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The combination of multiple sources of sensor and other data correlated together simultaneously to help provide situational awareness for autonomous vehicles and other devices
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Match the Dataset terms to the definitions [drag term to the definition]
Prepared, cleaned, and possibly labeled datasets used for machine learning model development. Usually around 70% of the overall dataset.
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Used to tweak a model’s hyperparameters and make sure model is optimized. Usually around 15-20% of the overall dataset.
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Needed to verify that the model to see if generalizes well for future, unseen data. Usually around 15-20% of the overall dataset.
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Data enhancement is one of the steps of a data engineering pipeline for AI