Ask HN: How to gain practical machine learning skills?
TLDR: I know the theoretical side of things in ML but lack practical experience. How can I fix that?
I am a CS student graduating in about half a year. I have taken a few ML classes in school. All of them went deep into the math (i.e., I know how things work, mostly) but very light on the "practical tricks" (i.e., I don't know how/where to use them). As a result, I do not have much experience writing code for ML stuff (save for some very simple assignments). For e.g., I have not used things like TensorFlow and PyTorch before.
What is your advice for me to gain practical ML skills? My gut feelings is that I need to do some exercises (a la leetcode). Where can I find these exercises? What other resources (e.g., books, courses, blogs, etc) should I look into?
Thanks and happy new year. :)
(
7 comments
| 20 points
| on HN
)
Once you've done a couple of challenges, try to find a task of your own and attempt to apply ML to it. Ideally go all the way and collect your own dataset. This will give you experience in formulating your problem, defining what is a good/acceptable solution and how a dataset can be created to solve it.
https://www.coursera.org/learn/machine-learning
As it says on the linked post I'm not learning just for knowledge sake but I very much intend to apply what I learn.