A former colleague of mine (SW guy) took Andrew's Coursera course, downloaded some Kaggle sets, fiddled with them, and put his Jupyter notebooks online. He learned the lingo of deep learning (no experience in them, though). Then he hit the interview circuit.
Got a senior ML position in a well known Fortune 500 company. Senior enough that he sets his goals - no one gives him work to do. He just goes around asking for data and does analyses. When he left our team he told me "Now that I have this opportunity, I can actually really learn ML instead of faking it."
If you think that's bad, you should hear the stories he tells at that company. Since senior leadership knows nothing about ML practices, practices are sloppy to get impressive numbers. Things like reporting quality based on performance on training data. And when going from a 3% to a 6% prediction success rate, they boast about "doubling the performance".
He eventually left for another company because it was harder to compete against bigger charlatans than he was.
Got a senior ML position in a well known Fortune 500 company. Senior enough that he sets his goals - no one gives him work to do. He just goes around asking for data and does analyses. When he left our team he told me "Now that I have this opportunity, I can actually really learn ML instead of faking it."
If you think that's bad, you should hear the stories he tells at that company. Since senior leadership knows nothing about ML practices, practices are sloppy to get impressive numbers. Things like reporting quality based on performance on training data. And when going from a 3% to a 6% prediction success rate, they boast about "doubling the performance".
He eventually left for another company because it was harder to compete against bigger charlatans than he was.