anishLearnsToCode/nlp-probabilistic-models

Solutions 📕 to coursera Course Natural Language Procesing with Probabilistic Models part of the Natural Language Processing 👨‍💻 Specialization ~deeplearning.ai

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This collection provides practical examples and code for understanding how computers process human language. It takes raw text or words as input and demonstrates how to build systems for autocorrection, part-of-speech tagging, and autocomplete. This is for anyone learning the foundations of natural language processing, particularly students or practitioners interested in text analysis and computational linguistics.

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Use this if you are a student or professional seeking to understand and implement fundamental probabilistic models for natural language processing tasks like autocorrect and autocomplete.

Not ideal if you are looking for a ready-to-use application or a high-level library for advanced NLP without needing to understand the underlying algorithms.

natural-language-processing computational-linguistics text-analysis language-modeling information-retrieval
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Jupyter Notebook

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MIT

Last pushed

Nov 14, 2020

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