|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Spacy||26,214||1,533||842||5 days ago||196||April 05, 2022||107||mit||Python|
|💫 Industrial-strength Natural Language Processing (NLP) in Python|
|Mindsdb||16,305||3||1||21 hours ago||42||March 19, 2019||621||gpl-3.0||Python|
|MindsDB is a Server for Artificial Intelligence Logic. Enabling developers to ship AI powered projects to production in a fast and scalable way.|
|Ciphey||13,422||4 months ago||50||June 06, 2021||52||mit||Python|
|⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡|
|500 Ai Machine Learning Deep Learning Computer Vision Nlp Projects With Code||12,490||7 days ago||19|
|500 AI Machine learning Deep learning Computer vision NLP Projects with code|
|Allennlp||11,300||117||67||6 months ago||264||April 14, 2022||94||apache-2.0||Python|
|An open-source NLP research library, built on PyTorch.|
|Ml Youtube Courses||10,294||a month ago||2||cc0-1.0|
|📺 Discover the latest machine learning / AI courses on YouTube.|
|Haystack||8,949||2||21 hours ago||29||July 06, 2022||355||apache-2.0||Python|
|:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.|
|Ml Visuals||8,380||4 months ago||13||mit|
|🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.|
|Nemo||6,805||2||5||a day ago||58||July 01, 2022||91||apache-2.0||Python|
|NeMo: a toolkit for conversational AI|
|Stanza||6,651||2||68||a day ago||17||April 23, 2022||80||other||Python|
|Official Stanford NLP Python Library for Many Human Languages|
Free hands-on course with the implementation (in Python) and description of several Natural Language Processing (NLP) algorithms and techniques, on several modern platforms and libraries.
Although it is not intended to have the formal rigor of a book, we tried to be as faithful as possible to the original algorithms and methods, only adding variants, when these were necessary for didactic purposes.
The best way to get the most out of this course is to carefully read each selected problem, try to think of a possible solution (language independent) and then look at the proposed Python code and try to reproduce it in your favorite IDE. If you already have knowledge of the Python language, then you can go directly to programming your solution and then compare it with the one proposed in the course.
If you want to play with these notebooks online without having to install any library or configure hardware, you can use the following service:
Natural Language Processing project with Python frameworks. NLP is a discipline where computer science, artificial intelligence and cognitive logic are intercepted, with the objective that machines can read and understand our language for decision making.
Books in plain text, both in English and Spanish. The enrichment of the entities is done from DBpedia.
conda install -c conda-forge spacy python -m spacy download en_core_web_sm python -m spacy download es_core_news_sm conda install -c conda-forge sparqlwrapper pip install pyspellchecker conda install -c anaconda gensim conda install -c conda-forge wordcloud conda install -c conda-forge stanza
Any kind of feedback/suggestions would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc...). If you want to make a contribution to the course you can do it through a PR.
This project is licensed under the terms of the MIT license.
I would like to thank Project Gutenberg for sharing the books in English and Peter Norvig for the spell checker algorithm.