More to come! Stay tuned ... 

btw: if you have something to be added, shoot me an email: keshtkaf[@]stjohns[.]edu

PAPERS-READING:

• Papers with Code in ML/NLP: https://paperswithcode.com/

• Dataset: https://paperswithcode.com/datasets

• ACL Anthology: https://www.aclweb.org/portal/

• FOJ-FLAIRS: https://journals.flvc.org/FLAIRS/index

• FLAIRS [1988-Present]: https://www.flairs.com/proceedings

• Arxiv: https://arxiv.org

• Machine Learning Glossary: https://developers.google.com/machine-learning/glossary

DATA 

• Data, software, equipment, and library resources [link]

• https://paperswithcode.com/datasets

• UCI Dataset: https://archive.ics.uci.edu/ml/index.php

• IMDB Dataset: https://www.imdb.com/interfaces/

• Brown Corpus:  https://www.kaggle.com/nltkdata/brown-corpus

• Kaggel:  https://www.kaggle.com/datasets

• Google Dataset Search: https://datasetsearch.research.google.com

• Earth Data: https://earthdata.nasa.gov/

• Amazon AWS: https://registry.opendata.aws/

• Azure Open Datasets: https://docs.microsoft.com/en-us/azure/open-datasets/dataset-catalog

• FBI Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/pages/home

• Data World: https://data.world/

• CERN Open Data Portal: http://opendata.cern.ch/

• Data Hub: https://datahub.io/search

• Awesome Dataset: https://github.com/awesomedata/awesome-public-datasets

• Health data : https://healthdata.gov/

• 25 Machine Learning Open Datasets: https://opendatascience.com/25-excellent-machine-learning-open-datasets/

• 20 Machine Learning Open Dataset https://odsc.medium.com/20-open-datasets-for-natural-language-processing-538fbfaf8e38

• IBM Dataset: https://developer.ibm.com/technologies/artificial-intelligence/data/

• NLP Dataset:  https://metatext.io/datasets

• Covid-19 tweet: https://www.kaggle.com/uracilo/twitter-covid19

NLP RESOURCES

• NLP Resources, collected by Roma Patel

• Wordnet: https://wordnet.princeton.edu/

• Wordnet SpaCy: https://spacy.io/universe/project/spacy-wordnet

• NLP Wiki [link]

• Linguistic resource [link]

• ACL [link]

• List of NLP/CL courses [link]

• Stanford NLP Group [link]

• Columbia NLP Group [link]

• Cornell NLP Group [link]

• ACM Special Interest Groups [link]

• Call for Paper on NLP [link]

• SIGNLL (event) [link]

• Call for Paper on AI [link]

• A Wiki for Calls For Papers (WIKICFP) [link]

• Linguistic List [link]

• NLP Dictionary [link]

• PySimpleGUI [Link]

• NodeJS: [Link]

• Linux cheatsheet: https://linoxide.com/linux-commands-cheat-sheet/

Python COURSES AND BOOKS ETC:

• Speech and Language Processing (Jurafsky): [link]

• Foundations of Statistical Natural Language Processing(Manning): [link]

• Introduction to Information Retrieval (Manning): [link]

• Dive into Deep Learning: https://d2l.ai/index.html

• Probabilistic Machine Learning: An Introduction [link]

• An Introduction to Statistical Learning: [link]

• Computer Science courses with video lectures: https://github.com/Developer-Y/cs-video-courses

• Reinforcement  Learning in Python: https://github.com/nicknochnack/ReinforcementLearningCourse

• PyTorch course: [Link]

• NLP with SpaCY: https://course.spacy.io/en/

• Keras NLP: https://keras.io/examples/nlp/

• Scientific Computing in Python: https://sebastianraschka.com/blog/2020/numpy-intro.html

• Statistics and Machine Learning in Python; book:  https://duchesnay.github.io/pystatsml/

• Python Data Science Handbook:  https://jakevdp.github.io/PythonDataScienceHandbook/

• Python for beginners, Mosh: https://www.youtube.com/watch?v=_uQrJ0TkZlc&ab_channel=ProgrammingwithMosh

• Introduction to Probability for Data Science: https://probability4datascience.com/index.html

• Dive into Deep Learning: https://d2l.ai/d2l-en-pytorch.pdf

• OpenCV: https://learnopencv.com/getting-started-with-opencv/

• OpenCV Tutorial: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html

• ML with Python, Rune: [Link]

• React with Mosh: [Link]

PYTHON

• Awesome Python: https://github.com/vinta/awesome-python

• Project-Based Learning: https://github.com/practical-tutorials/project-based-learning

• The Algorithms implemented in Python: https://github.com/TheAlgorithms/Python

• An Open Source Machine Learning for Everyone: https://github.com/tensorflow/tensorflow

•  Project-based Learning: https://github.com/practical-tutorials/project-based-learning

• 100+ Python challenging programming exercises: https://github.com/zhiwehu/Python-programming-exercises

• Playground and cheatsheet for learning Python: https://github.com/trekhleb/learn-python

• Jupyter notebooks for teaching/learning Python 3: https://github.com/jerry-git/learn-python3

• A collection of useful scripts, tutorials, and other Python-related things: https://github.com/rasbt/python_reference

• Coding Problems: https://github.com/MTrajK/coding-problems

• PySimpleGUI [Link]

• Mike Driscoll: https://www.blog.pythonlibrary.org/

• Python 3 Module of the Week — PyMOTW 3: https://pymotw.com/3/

• NumPy: https://numpy.org/doc/stable/user/quickstart.html

• Pandas: https://pandas.pydata.org/docs/getting_started/index.html

• BeautifulSoup: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

• Keras: https://keras.io/examples/

• Scikit-Learn : https://scikit-learn.org/stable/getting_started.html

• OpenCV Tutorial: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html

• SciPy: https://scipy.org/

MACHINE LEARNING, NLP & AI

• ML and NLP Libraries in Python (and other): [Link]

• Scikit-learn : https://scikit-learn.org/stable/getting_started.html

• SpaCy: https://course.spacy.io/en/

• NLP with Tranformers: https://transformersbook.com/

• NLP with SpaCY: https://course.spacy.io/en/

• Keras NLP: https://keras.io/examples/nlp/

• Transformere, HugginFace: https://huggingface.co/docs/transformers/index

• Keras: https://keras.io/examples/

• Keras code example: https://keras.io/examples/?linkId=8025095

• Pytorch: https://pytorch.org/tutorials/

• Pytorch-lightning: https://pytorch-lightning.readthedocs.io/en/stable/index.html

• Pytorch-NLP: https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

• Exformers: https://github.com/facebookresearch/xformers

and Exformers with pytorch-lightning

• FastAI: https://docs.fast.ai/

• FastAI NLP: https://www.fast.ai/2019/07/08/fastai-nlp/

• Text Visualization: https://textvis.lnu.se/

• PyTorch NLP: https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

• NLTK: https://www.nltk.org/index.html

Reinforcement Learning | Full Course using Python

•  Course: [Link]

• Materials: https://github.com/nicknochnack/ReinforcementLearningCourse

• Stable Baselines 3: https://stable-baselines3.readthedocs...

• OpenAI Gym: https://gym.openai.com/

• PyTorch: https://pytorch.org/

• Atarimania ROMs: http://www.atarimania.com/roms/Roms.rar

• Swig: http://www.swig.org/Doc1.3/Windows.html

NN with Example: 

• https://www.gormanalysis.com/blog/neural-networks-a-worked-example/

DATA MINING /EDUCATIONAL DATA MINING

INTELLIGENT TUTORING SYSTEMS(ITS)

ROBOTICS

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ACM

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IEEE

LaTex

• Overleaf: https://www.overleaf.com/login

• Texpad: https://www.texpad.com

WRITING RESEARCH PAPER, THESIS, ETC.

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