[D] No-nonsense, comprehensive reading list for ML & DS
I’m a self-taught ML practitioner, working in the industry. However, I feel that my lack of formal education is hurting me, especially when working with research folks (stats & math heavy topics).
I’m quite good at self-learning, so I’d like to revisit all the foundations in the next 12 months. I’m looking to have solid, comprehensive grasp of the most important topics in ML, so that I can at least understand conversations around me.
Would appreciate suggestions on how improve the reading list I cooked up (what to add / remove / replace).
Do you think 12 months is a reasonable timeline for the following?