5 ms·

I don't mean to imply it's impossible, or throw out some appeal to authority, but dabbling in ML is not going to open doors for you. I worked in and around the

mindcrime 8y ago
I don't mean to imply it's impossible, or throw out some appeal to authority, but dabbling in ML is not going to open doors for you. I worked in and around the field for more than a decade. Companies who are serious about this stuff can sniff a beginner out and they tend to want credentials. What I think you're missing is that ML is becomeing accessible to organizations WAY outside the sphere you're referring to. In the not-too-distant future, "Bob's Small Engine Repair and Screen Doors" will want to take advantage of ML / Data Science / AI. And it may well be the case that all they need is somebody that can implement Linear Regression or K-Means using SK-Learn, or using a cloud ML API. And larger organizations will always have room for people with varying levels of skill, especially as demand for the most highly skilled people outstrips the available supply. and knowing how to throw a classifier together does not in any way mean you're competent and able to solve hard problems. Not everybody is solving a "hard problem". By way of analogy... if your car has a broken connecting rod, you need to take it to a mechanic who knows how to pull an engine, tear it down, rebuild it from scratch and put everything back together. If you have a bad O2 sensor, that work can be done by somebody much less knowledgeable. If you need the oil changed, that can be done by somebody even less knowledgeable still. Or to use a different analogy, you don't need a neurosurgeon to cut out an ingrown toenail. Use the right person for the job at hand.
thisisit 8y ago
> Companies who are serious about this stuff What about companies who are not serious about this stuff? There are tons of companies who are not actually trying to solve a hard ML problem. They just want their own recommendation or prediction engines. That doesn't require any deep understanding or 5-10 years in this field. A ML puritan might say that is not real or serious stuff and this is all fad. That might be true. But I think that is one of the ways someone can start off their 5-10 years journey - working on simpler problems and getting paid.

mindcrime 8y ago

I don't mean to imply it's impossible, or throw out some appeal to authority, but dabbling in ML is not going to open doors for you. I worked in and around the field for more than a decade. Companies who are serious about this stuff can sniff a beginner out and they tend to want credentials. What I think you're missing is that ML is becomeing accessible to organizations WAY outside the sphere you're referring to. In the not-too-distant future, "Bob's Small Engine Repair and Screen Doors" will want to take advantage of ML / Data Science / AI. And it may well be the case that all they need is somebody that can implement Linear Regression or K-Means using SK-Learn, or using a cloud ML API. And larger organizations will always have room for people with varying levels of skill, especially as demand for the most highly skilled people outstrips the available supply. and knowing how to throw a classifier together does not in any way mean you're competent and able to solve hard problems. Not everybody is solving a "hard problem". By way of analogy... if your car has a broken connecting rod, you need to take it to a mechanic who knows how to pull an engine, tear it down, rebuild it from scratch and put everything back together. If you have a bad O2 sensor, that work can be done by somebody much less knowledgeable. If you need the oil changed, that can be done by somebody even less knowledgeable still. Or to use a different analogy, you don't need a neurosurgeon to cut out an ingrown toenail. Use the right person for the job at hand.

thisisit 8y ago

> Companies who are serious about this stuff What about companies who are not serious about this stuff? There are tons of companies who are not actually trying to solve a hard ML problem. They just want their own recommendation or prediction engines. That doesn't require any deep understanding or 5-10 years in this field. A ML puritan might say that is not real or serious stuff and this is all fad. That might be true. But I think that is one of the ways someone can start off their 5-10 years journey - working on simpler problems and getting paid.