The responses confuse "machine learning" with neural networks so far as I can see. I do lots of statsy things: good luck with that outside of R.
https://alan-turing-institute.github.io/MLJ.jl/dev/about_mlj...
The ecosystem in Julia is quite strong for MCMC libs because people do not have to lower something to C++ to develop such a library: https://discourse.julialang.org/t/mcmc-landscape/25654/
Of course, for users, they might prefer something like Turing, but I think the Julia tends to blur the differences between user and developer more so than in most other languages (for good or worse) since everything is in one language.
There's always RCall for R inside of Julia, the best of both worlds.
Only because doing "AI" gets you bigger grants.
Machine learning Statistics
network, graphs model
weights parameters
learning fitting
generalization test set performance
supervised learning regression/classification
unsupervised learning density estimation, clustering
large grant = $1,000,000 large grant = $50,000
nice place to have a meeting: nice place to have a meeting:
Snowbird, Utah, French Alps Las Vegas in AugustWhile R definitely leads the pack on variety of useful stats packages, there are excellent tools for certain tasks in other languages. I do a lot of probabilistic programming, and PyMC in Python and Turing in Julia are excellent packages. And both languages have official Stan interfaces. Not knowing R has not been a problem for me.
https://www.google.com/search?q=%22statistics+is+a+subset+of...
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Machine learning is a subset of statistics.