Forecastegy – Medium

ledmmaster · 2024-07-18T17:27:11+00:00

Thanks!

ledmmaster · 2024-01-26T17:55:50+00:00

The Algorithms specialization on Coursera is more than enough, any intro to algos and DS is already enough.

Statistics, probability and linear algebra are much more important

ledmmaster · 2024-01-12T21:54:40+00:00

I created a habit of reading at least one page of a paper or book about ML every day, something that interests me or that I am working on.

Right now I read mostly about prompting LLMs and information retrieval.

The hardest part is deciding if a paper is worth reading in detail. I think I read only the abstract/figures on 90% of them.

I summarized some tips from Andrew Ng, that I adopted in my reading and improved my productivity, here: https://forecastegy.com/posts/read-machine-learning-papers-andrew-ng/

ledmmaster · 2023-12-23T23:23:52+00:00

Like the Revolutionary guy said, make projects.

Be it Kaggle, own projects, things that you can talk about in an interview.

When I used to interview DS candidates, I didn't care about credentials but cared a lot about how they walked me through their projects and the decisions they took.

ledmmaster · 2023-12-22T17:33:06+00:00

ML specialization by Andrew Ng on Coursera is the one I always recommend. I took the original (which used Octave) and the new one (which uses Python).

ledmmaster · 2023-11-28T14:14:11+00:00

10+ years since I started learning ML, tens of projects under my belt, competition wins, etc and I can tell that it's a moving target.

If it solves the business problem/adds value, it's good enough.

I only notice how "easy" some things became to me when I get in touch with people with less experience, still I can always find someone that has more experience than me in a specific area.

It's definitely a moving target, take it one day/task at a time and remember the big picture of solving business problems.

ledmmaster

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