Tons of resources online will get you distracted a lot, a good way is to have your own learning path and keep focus. I got this idea from two people:

• https://www.coxy1989.com/curriculum.html
• http://karlrosaen.com/ml/.

Thanks a lot to them for sharing their own experiences.

This blog is a guideline that I will continue update. Side by side, I may post learning logs with details on what I learned.

This is to lay a good foundation for later machine learning, which includes:

Probability and Statistics

Bloomberg ML EDU course which I am following at this moment: https://bloomberg.github.io/foml/#about
More resources on statistics: https://cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15/index.html
Problems and solutions: http://karlrosaen.com/ml/hw/

Some supporting materials:

• Mathematics background check: https://davidrosenberg.github.io/mlcourse/Notes/prereq-questions/math-questions.pdf
• Simple statistics crib-sheet: http://www.gatsby.ucl.ac.uk/teaching/courses/ml1-2008/cribsheet.pdf

Machine learning 101

Machine learning by Andrew Ng: https://www.coursera.org/learn/machine-learning
Python Machine Learning: https://github.com/rasbt/python-machine-learning-book-2nd-edition

There are some good podcasts:

• Talking machine podcasts:https://www.thetalkingmachines.com/
• Becoming a data scientist podcasts: https://www.becomingadatascientist.com/category/podcast/
• The Master Algorithm: https://player.fm/series/data-skeptic/the-master-algorithm

To complete this stage, 5 months are recommended.

Stage 2 - Applied