|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Keras||58,563||330||5 hours ago||68||May 13, 2022||391||apache-2.0||Python|
|Deep Learning for humans|
|Scikit Learn||54,525||18,944||6,722||13 hours ago||64||May 19, 2022||2,193||bsd-3-clause||Python|
|scikit-learn: machine learning in Python|
|Ml For Beginners||49,272||a day ago||12||mit||Jupyter Notebook|
|12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all|
|Made With Ml||33,193||a month ago||5||May 15, 2019||11||mit||Jupyter Notebook|
|Learn how to responsibly develop, deploy and maintain production machine learning applications.|
|Spacy||26,334||1,533||842||a day ago||196||April 05, 2022||107||mit||Python|
|💫 Industrial-strength Natural Language Processing (NLP) in Python|
|Ray||25,975||80||199||9 hours ago||76||June 09, 2022||2,886||apache-2.0||Python|
|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.|
|Streamlit||25,202||17||404||10 hours ago||182||July 27, 2022||640||apache-2.0||Python|
|Streamlit — A faster way to build and share data apps.|
|Data Science Ipython Notebooks||25,025||a month ago||33||other||Python|
|Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.|
|Applied Ml||24,242||16 days ago||3||mit|
|📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.|
|Roadmap to becoming an Artificial Intelligence Expert in 2022|
Welcome! The purpose of this repository is to serve as stockpile of statistical methods, modeling techniques, and data science tools. The content itself includes everything from educational vignettes on specific topics, to tailored functions and modeling pipelines built to enhance and optimize analyses, to notes and code from various data science conferences, to general data science utilities. This will remain a work in progress, and I welcome all contributions and constructive criticism. If you have a suggestion or request, please use the "Issues" tab and I will endeavor to respond expeditiously!
Note: GitHub often has trouble rendering larger .ipynb files in particular. If you find that you are unable to view one of the jupyter notebooks linked below, I recommend copy and pasting the result into jupyter's nbviewer, which will take you to a viewable link like this one here for my "Visualization with Plotly" notebook. Note that if you want to ensure that you are viewing the most up-to-date version of the notebook with nbviewer, you should add
?flush_cache=true to the end of the generated URL as is described here; otherwise, your link risks being slightly out-of-date.
All are welcome and encouraged to contribute to this repository. My only request is that you include a detailed description of your contribution, that your code be thoroughly-commented, and that you test your contribution locally with the most recent version of the master branch integrated prior to submitting the PR.