Awesome
Learning ML (Machine Learning) 
Just one of the things I'm learning. https://github.com/hchiam/learning
Some favourites
https://github.com/hchiam/learning-tf/tree/master/my_coursera_notes
https://github.com/hchiam/machineLearning/blob/master/more_notes/googleMLCrashCourse.md
https://github.com/hchiam/machineLearning/blob/master/more_notes/misnomersAndConfusingTerms.md
https://github.com/hchiam/machineLearning/blob/master/more_notes/reinforcement_learning.md
https://github.com/hchiam/learning-prompt-eng
https://github.com/hchiam/learning-gpt4all
https://github.com/hchiam/learning-tfjs-umap
https://github.com/hchiam/comment-analysis
In no particular order
https://github.com/hchiam/machinelearning
https://github.com/hchiam/webApp_MachineLearning_Gesture
https://github.com/hchiam/learning-pytorch
https://github.com/hchiam/learning-automl
https://github.com/hchiam/learning-tensorflow
https://github.com/hchiam/text-similarity-test-microservice
https://github.com/hchiam/learning-google-assistant
https://github.com/hchiam/text-similarity-test
https://github.com/hchiam/learning-annoy
https://github.com/hchiam/cogLang-geneticAlgo
https://github.com/hchiam/python-ml-web-app
https://github.com/hchiam/crash-course-ai-labs
https://github.com/hchiam/ai_for_robotics
https://github.com/afshinea/stanford-cs-230-deep-learning --> use as a summary of the key points, but also add your own notes to fill in your own curiosity/knowledge gaps (=active learning), to learn faster while taking Andrew Ng's Coursera course on Machine Learning offered by Stanford: https://www.coursera.org/learn/machine-learning
- https://github.com/hchiam/learning-octave may be helpful for that Coursera course too