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Core
Label Noise and Weak Supervision
Label noise means mistakes in your training labels, and it sets a hard ceiling on model accuracy regardless of how strong the architecture is. Weak supervision generates training labels through code (labeling functions, distant supervision) rather than by hand, giving up some accuracy in return for scale. AI, ML, and GenAI engineer interviews probe this because real datasets are messy, the gap between a model stuck at 78 percent and one hitting 90 percent usually comes down to labels rather than the model, and candidates who grasp confident learning and clean test sets are the ones who genuinely move metrics.
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PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceYou suspect your training labels are noisy. How do you detect it and train a good model anyway?→SQL & Data EngineeringWhat data-quality checks do you put on a pipeline, and how do you catch bad data before it spreads?→SQL & Data EngineeringHow do you find and remove duplicate rows in SQL, including 'fuzzy' near-duplicates?→LLM & GenAI FundamentalsHow do you prepare a dataset to fine-tune an LLM, and why does data quality dominate?→LLM & GenAI FundamentalsHow and when do you use synthetic data (LLM-generated) for training or fine-tuning?→Machine Learning & Data ScienceWhat are active learning and semi-supervised learning, and when do you use them?→
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