Hacker News new | past | comments | ask | show | jobs | submit login

Since the "How Do I Learn AI/ML" question pops up on Hacker News once a month (most recent: https://news.ycombinator.com/item?id=16138353), here's my comments:

Yes, Al/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessable. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry.

However, contrary to the thought pieces that tend to pop up, taking and passing a MOOC doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats appling the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process.

Reviewing the Keras documentation (https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion.




I'd suggest fast.ai mooc before keras docs. I took Hintons course and tried to learn through tf/keras docs, but wasn't able to really get going until I found fast.ai. Some of the best "classes" I've ever watched and there's a ton of people helping in the forums.


While I agree that PhD gate-keeping is frustrating, I've found a sizable subset of the people who say that really mean "we want you to have the mathematical foundation for this", not "we require a PhD". I don't have a PhD, but I've found that generally, as long as I can show I have the theory down, employers don't seem to mind.


The PhD reference in the comment is more toward other comments made on Hacker News/Reddit/Medium thought pieces.

For job hunting, the credibility issue is more nuanced, and I wrote a separate rant about that a couple weeks ago: https://twitter.com/minimaxir/status/951117788835278848


I do agree that for applying technologies in real world problems, a much deeper understanding is required than what majority of MOOCs provide. That being said, different learning methods suit different type of learners. For some, starting out with a hands-on overview of the topic at hand works best. This is where tutorials such as this one shine through.


> Reviewing the Keras documentation (https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion.

Wholeheartedly agree. It’s simple to setup a Bidirectional LSTM and run it with PyTorch if you are trying to predict time series data.




Consider applying for YC's Spring batch! Applications are open till Feb 11.

Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: