✨ 5 Free Resources for Learning Natural Language Processing with Python 🚀

This page summarizes the projects mentioned and recommended in the original post on /r/learnmachinelearning

Nutrient - The #1 PDF SDK Library
Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
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CodeRabbit: AI Code Reviews for Developers
Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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  1. nlp-tutorial

    Natural Language Processing Tutorial for Deep Learning Researchers

  2. Nutrient

    Nutrient - The #1 PDF SDK Library. Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.

    Nutrient logo
  3. nlp-recipes

    Discontinued Natural Language Processing Best Practices & Examples

  4. nlp_course

    YSDA course in Natural Language Processing

    🔗 https://github.com/yandexdataschool/nlp_course

  5. nlp-in-python-tutorial

    comparing stand up comedians using natural language processing

    🔗 https://github.com/adashofdata/nlp-in-python-tutorial

  6. awesome-nlp

    :book: A curated list of resources dedicated to Natural Language Processing (NLP)

  7. CodeRabbit

    CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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