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Good day! This is my first time going to such an event; and I was wondering if there were any event veterans here who could offer any advice as to how can I maximize learning in the said event.

P.S. This is the Singapore AI School : https://aisummerschool.aisingapore.org/

We all use Jupyter notebooks, Pytorch/Tensorflow/etc.

What libraries, plugins, IDE’s or other things do you use that others could find useful?

Say I am predicting smokers in my dataset, and from prior knowledge I know that 15% of adults in the U.S. are smokers. My end goal is to deploy my model into a database that has information on 200+ million adults in the U.S to find potential smokers for a marketing campaign.

For my modeling data, should I purposefully mimic the “distribution” of smokers in the U.S. and have 15% of the data be smokers, and 85% of the data be non-smokers? Most of my coworkers have said “it’s easier to just balance them” but I believe this model would not be generalizable to the entire U.S. population if I keep it balanced.

I recently completed my bachelor’s in Computer Science and have been working as a Machine Learning Engineer for the past 5 months. Now I’m looking to transition into a bigger company in a similar role.

Background:

I have a bachelor’s degree in Computer Science from a mid-tier public university in the United States. During college, I participated in a summer REU in ML. I have co-authored 3 publications (two as second author) and published them at top AI conferences.

My initial plan was to go straight for a PhD in Machine Learning, but I was too ambitious with the schools I picked, and I ended up being rejected by all of them.

Luckily, in the meantime, I landed a job as a ML Engineer and I’ve been working in this position for the past 5 months. In the time that I’ve worked, I have realized that I like writing code and putting things into production slightly more than hardcore research. I like my job, but I’d like to transition into a bigger company with a more established Data Science/ML team.

Here’s where I’d like to hear my fellow redditors’ thoughts.

I’m debating whether I should consider doing a Master’s in Machine Learning, get that degree, and then target the big companies? Or can I make up for the lack of an advanced degree through work experience?

As you all probably know, most of the job postings that I see expect the candidate to have a Master’s degree as a minimum, or a PhD. I understand where they come from, as I’m aware that nothing can substitute the knowledge depth you gain by going through the rigor of grad school.

I was in the fast track master’s program during my bachelor’s, and I’m only 2 semesters away from getting my master’s in C.S with a concentration in Data Science. The reason I didn’t continue is because I knew for a fact that 1) I could learn more by working and 2) the quality of the coursework is not great. On the plus side of doing the master’s, I have a good relationship with a few professors who are pretty involved in ML research, and I could do a research based master’s with a thesis and boost my profile.

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What are your thoughts?