Ask yourself: Do you really need ML to solve the problems you're interested in solving?
If you're learning it for career purposes, keep in mind that many corporate ML use-cases are problematic at best. At worst, you will produce something that kills someone inadvertently, possibly more than one person.
Learn about the many pitfalls and limitations of ML. Learn about inadvertent bias in datasets. Learn about the issues with inputs not represented (or not adequately represented) in your training dataset.
Most importantly, understand that ML is not magic and without significant guardrails in place, there's a good chance something will fuck up.
If you're learning it for career purposes, keep in mind that many corporate ML use-cases are problematic at best. At worst, you will produce something that kills someone inadvertently, possibly more than one person.
Learn about the many pitfalls and limitations of ML. Learn about inadvertent bias in datasets. Learn about the issues with inputs not represented (or not adequately represented) in your training dataset.
Most importantly, understand that ML is not magic and without significant guardrails in place, there's a good chance something will fuck up.