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Answer: Word Embeddings
Word Embeddings is the correct technique for converting textual data into numerical vectors. This method represents words as dense vectors in a continuous vector space, effectively capturing semantic relationships between words. It is particularly useful for maintaining contextual information during the conversion process. Databricks MLlib supports this and other text processing tasks, making it a suitable choice for your machine learning projects.
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Your team is working on a machine learning project that involves preprocessing textual data by transforming words into numerical vectors. Which of the following techniques, supported by Databricks MLlib, is appropriate for this conversion?
A
Tokenization
B
Text Encoding
C
Word Embeddings
D
Feature Extraction