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Monitoring School Meal Quality - 2
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Module 4
Data Strategy for Real-World Messiness
1
Identifying Noise & Irrelevance in Social Media Data
Identify common types of noise and irrelevance in scraped social media data for this project.
2
Data Filtering: Ensuring Quality and Relevance
Define and apply filtering criteria to remove low-quality or off-topic samples from both text and image data.
Weak Supervision and Noisy Label Generation
Explain the concept of weak supervision and describe how heuristic labeling functions could generate noisy labels at scale.
Iterative Label Quality Improvement with Model-Assisted Relabeling
Describe a practical strategy for iteratively improving label quality as the project progresses, such as model-assisted relabeling.
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