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0. Doing ML requires an understanding of math. One gets less mileage from making magic incantations w/o understanding underlying implementation, compared with t
by ssivark 9y ago
0. Doing ML requires an understanding of math. One gets less mileage from making magic incantations w/o understanding underlying implementation, compared with the mileage for front-end dev, as an example.
1. Any non-trivial ML will require significant amount of computational power (RAM/CPU/GPU). Who will subsidize that for participants? It's not sufficient to have a laptop that can run a browser.
2. Notice the proliferation of data science bootcamps. Once the syllabus/infrastructure becomes streamlined (and cheap), I would expect there to be more online offerings.
That said, there might currently be a market need for a syllabus based on Kaggle or other freely available data sets, and free compute resources provided on cloud platforms.