How to Create a Dataset for Machine Learning

The video emphasizes the importance of data in AI systems, highlighting that many AI issues stem from data problems rather than the models themselves. It outlines the process of creating a data set, which involves data collection, cleaning, and labeling. The video discusses various methods for collecting data, including using existing data sets, crowdsourcing, and citizen science. It also covers data cleaning and augmentation techniques, such as using GANs. The process of data labeling is explored, noting its labor-intensive nature. Finally, the video explains the iterative process of improving data sets to enhance AI systems.

In what ways can crowdsourcing be utilized in data collection?

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What challenges might arise during the data labeling process?

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What should be done if issues are discovered during model training?

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