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"a lot of this stuff is better described as applied statistics" This is a key insight. Bravo! To generalize a bit more, most of ML is applied mathematics. Get

"a lot of this stuff is better described as applied statistics" This is a key insight. Bravo! To generalize a bit more, most of ML is applied mathematics. Getting a good grounding in the underlying math is the most illuminating step to learning ML (spoken as someone who wasted a lot of time doing other things thanks to an irrational fear of learning mathematics and am still bad at it) Deep math/stat understanding combined with the engineering bits(like programming, cleaning the data, running clusters) and the communication bits, (like visualization) brings you to (what should be) 'data science' (imvvho ymmv etc etc). I am still not sure one person can pull it all off-it probably needs a solid team of specialists. But hey 'data scientist' is a hot job description, and so you can't blame people who know bits and pieces (sometimes very small bits and pieces ;) ) calling themselves 'data scientists' or whatever. "Machine Learning for Hackers" and all that jazz. We've seen all this before with "HTML coders" from the nineties.

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robrenaud 14y ago
Work as a search quality engineer at Google and you do pretty much all of that. Except for running the clusters[1], I've done pretty much all of those steps myself. I started with a nice statistical idea, built some simple models, played with feature selection and learning algorithms, built model viewers, built classifiers, validated classifiers, built demos, validated demos, built a production implementation[2], optimized the production implementation to make it small/fast enough, and finally launched a big search quality improvement. [1] I certainly write distributed code that runs on them, but maintaining the DCs definitely isn't part of my job description. [2] Validation of the final quality in prod is actually someone else's job, not because I couldn't do it, but you might not want me to tell you how good my stuff is, cause you know, I might be biased.

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robrenaud 14y ago

Work as a search quality engineer at Google and you do pretty much all of that. Except for running the clusters[1], I've done pretty much all of those steps myself. I started with a nice statistical idea, built some simple models, played with feature selection and learning algorithms, built model viewers, built classifiers, validated classifiers, built demos, validated demos, built a production implementation[2], optimized the production implementation to make it small/fast enough, and finally launched a big search quality improvement. [1] I certainly write distributed code that runs on them, but maintaining the DCs definitely isn't part of my job description. [2] Validation of the final quality in prod is actually someone else's job, not because I couldn't do it, but you might not want me to tell you how good my stuff is, cause you know, I might be biased.