I've been trying to teach myself ML and AI for a while now, and though only tangentally relevant to the article perhaps others can take a few tips from my experience. First, I didn't really have a breakthrough until I ditched the 'recommended' textbooks and video courses and just started picking out a topic and learning by doing. Anything I don't understand or know when trying to implement it I just google/youtube/wikipedia it and keep messing with it until I know how it works at a conceptual level. Thats where resources like this article really come in handy.
For the heavier math parts, I just write a short summary of the formula and what its various parameters do and try to make a mental note of it. I certainly do not try to use the formulas to solve complex mathematical problems or write an implementation in python. I chalk this task up to `someday when I have the time`.
Finally, I'll get a dataset and try to solve various problems using the new skill I just learned using R/python & co.
Thats it.
This method has ridiculously accelerated the speed at which I've been able to acquire ML/AI skills that I also know how to apply in the real world. Before I felt like I was moving at a snails pace.
This method might not work well for everyone but its at least an interesting alternative to most of the recommendations of doing A,B,C online courses and reading X,Y,Z books.
Your approach resonates with me. I will recommend Practical Deep Learning For Coders[1]. This course is taught by Jeremy Howard who won kaggle competition for 2 consecutive years. His motto is to "Make Deep Learning Uncool again"
I personally found it very hands on, it jumps into practical application right of the bat which helps keeping the motivation steady. Having said that, its not easy or dumbed down in any sense.
I've been trying to teach myself ML and AI for a while now, and though only tangentally relevant to the article perhaps others can take a few tips from my experience. First, I didn't really have a breakthrough until I ditched the 'recommended' textbooks and video courses and just started picking out a topic and learning by doing. Anything I don't understand or know when trying to implement it I just google/youtube/wikipedia it and keep messing with it until I know how it works at a conceptual level. Thats where resources like this article really come in handy.
For the heavier math parts, I just write a short summary of the formula and what its various parameters do and try to make a mental note of it. I certainly do not try to use the formulas to solve complex mathematical problems or write an implementation in python. I chalk this task up to `someday when I have the time`.
Finally, I'll get a dataset and try to solve various problems using the new skill I just learned using R/python & co.
Thats it.
This method has ridiculously accelerated the speed at which I've been able to acquire ML/AI skills that I also know how to apply in the real world. Before I felt like I was moving at a snails pace.
This method might not work well for everyone but its at least an interesting alternative to most of the recommendations of doing A,B,C online courses and reading X,Y,Z books.