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In this video tutorial, Amun, a data scientist, discusses the importance of cleaning data in natural language processing. He points out a spelling error in the text to show how natural language looks. Cleaning data is a fundamental step in the data science pipeline, and while techniques like missing value and outlier treatment are used for numerical data, text data requires different cleaning techniques. The video covers basic and advanced cleaning techniques, emphasizing the necessity of understanding and implementing basic cleaning first.