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

https://github.com/DragonflyStats/Coursera-ML

https://github.com/hchiam/learning-github-copilot