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Vectorization converts text into numerical vectors that machines can understand, and it’s one of the most important concepts covered in an AI Learning Courses. Since computers cannot process raw text directly, vectorization transforms words into meaningful numeric representations used for training ML and NLP models.
Common vectorization methods include:
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Bag of Words
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TF-IDF
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Word2Vec
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Transformers embeddings (BERT-style)
These techniques help machines capture relationships, context, and meaning from text, making them essential skills taught in modern ai learning courses.
Topic starter
Posted : 13/11/2025 11:33 am