Codecademy's model tends to be very poor for topics that are sufficienty theoretically complex, as the turnaround time between exercise beginning and conclusion appears to be about 5 minutes for non-project work.
Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent.
Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.
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.
Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path.
Here are the resources I found useful:
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Advices from Open AI, Facebook AI leaders
Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)
From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora
In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent.
Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.