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From Raw to Refined: Understanding Preprocessing, Cleaning, and Labeling in Data Preparation
This story was originally published on HackerNoon at: https://hackernoon.com/from-raw-to-refined-understanding-preprocessing-cleaning-and-labeling-in-data-preparation.
Learn about techniques like tokenization, part-of-speech tagging, and feature extraction, ensuring your dataset is optimized for various tasks.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.
You can also check exclusive content about #ai-training-data, #data-provenance, #mitigating-bias-in-ai, #ai-transparency, #ai-ethics, #machine-learning-models, #datasheets-for-datasets, #ai-data-documentation, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
and for more stories, please visit hackernoon.com.
Preprocessing, cleaning, and labeling are crucial steps in data preparation, involving techniques like tokenization, feature extraction, and handling missing values. Ensuring compatibility with intended tasks, these processes optimize the dataset for analysis and model training.
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By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/from-raw-to-refined-understanding-preprocessing-cleaning-and-labeling-in-data-preparation.
Learn about techniques like tokenization, part-of-speech tagging, and feature extraction, ensuring your dataset is optimized for various tasks.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.
You can also check exclusive content about #ai-training-data, #data-provenance, #mitigating-bias-in-ai, #ai-transparency, #ai-ethics, #machine-learning-models, #datasheets-for-datasets, #ai-data-documentation, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
and for more stories, please visit hackernoon.com.
Preprocessing, cleaning, and labeling are crucial steps in data preparation, involving techniques like tokenization, feature extraction, and handling missing values. Ensuring compatibility with intended tasks, these processes optimize the dataset for analysis and model training.