|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Keras||58,521||330||18 hours ago||68||May 13, 2022||390||apache-2.0||Python|
|Deep Learning for humans|
|Scikit Learn||54,480||18,944||6,722||17 hours ago||64||May 19, 2022||2,196||bsd-3-clause||Python|
|scikit-learn: machine learning in Python|
|Ml For Beginners||48,853||a day ago||13||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||26,281||1,533||842||17 hours ago||196||April 05, 2022||111||mit||Python|
|💫 Industrial-strength Natural Language Processing (NLP) in Python|
|Ray||25,903||80||199||17 hours ago||76||June 09, 2022||2,894||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||25,099||17||404||18 hours ago||182||July 27, 2022||628||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||11 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|
The first half is more or less my learning path in the past two years while the second half is my plan for this year. I tried to make a balance between comprehension and doability. For more extensive lists, you can check Github search or CS video lectures
Hope the list is helpful, especially to whom are not in CS major but interested in data science!
You may find the list overwhelming. Here is my suggestion if you want to have some basic understanding in one month:
Statistical Learning is the introduction course. It is free to earn a certificate. It follows Introduction to Statistical Learning book closely. Coursera Stanford by Andrew Ng is another introduction course course and quite popular. Taking either of them is enough for most of data science positions. People want to go deeper can take 229 or 701 and read ESL book.
The basic NLP course by Stanford is the fundamental one. SLP 3ed follows this course. After this, feel free to take one of the three NLP+DL courses. They basically cover same topics. The Stanford one have HWs available online. CMU one follows Goldberg's book. Deepmind one is much shorter.
Some other people's collections: NLP, DL-NLP, Speech and NLP, Speech, RNN
Ng's courses are already good enough. Reading Part 2 of Goodfellow's book can also be helpful. Learning one kind of DL packages is important, such as Keras, TF or Pytorch. People may choose a focus, either CV or NLP. People want to have deeper understanding of DL can take Hinton's course and read Part 3 of Goodfellow's book. Fast.ai has very practical courses.