Update TuringLang homepage url (#1954)

Updated broken links (turing.ml --> turinglang.org)

Author: jamesmaino

README.md

@@ -3,14 +3,14 @@
 [![Build Status](https://github.com/TuringLang/Turing.jl/workflows/Turing-CI/badge.svg)](https://github.com/TuringLang/Turing.jl/actions?query=workflow%3ATuring-CI+branch%3Amaster)
 [![Coverage Status](https://coveralls.io/repos/github/TuringLang/Turing.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/Turing.jl?branch=master)
 [![codecov](https://codecov.io/gh/TuringLang/Turing.jl/branch/master/graph/badge.svg?token=OiUBsnDQqf)](https://codecov.io/gh/TuringLang/Turing.jl)
-[![Documentation](https://img.shields.io/badge/doc-latest-blue.svg)](https://turing.ml/dev/docs/using-turing/)
+[![Documentation](https://img.shields.io/badge/doc-latest-blue.svg)](https://turinglang.org/dev/docs/using-turing/)
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 
 **Turing.jl** is a Julia library for general-purpose [probabilistic programming](https://en.wikipedia.org/wiki/Probabilistic_programming_language). Turing allows the user to write models using standard Julia syntax, and provides a wide range of sampling-based inference methods for solving problems across probabilistic machine learning, Bayesian statistics, and data science. Compared to other probabilistic programming languages, Turing has a special focus on modularity, and decouples the modelling language (i.e. the compiler) and inference methods. This modular design, together with the use of a high-level numerical language Julia, makes Turing particularly extensible: new model families and inference methods can be easily added.
 
 Current features include:
 
-- [General-purpose](https://turing.ml/dev/tutorials/06-infinite-mixture-model/) probabilistic programming with an [intuitive modelling interface](https://turing.ml/dev/tutorials/00-introduction/)
+- [General-purpose](https://turinglang.org/dev/tutorials/06-infinite-mixture-model/) probabilistic programming with an [intuitive modelling interface](https://turinglang.org/dev/tutorials/00-introduction/)
 - Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions
 - [Particle MCMC](https://github.com/TuringLang/AdvancedPS.jl) sampling for complex posterior distributions involving discrete variables and stochastic control flows
 - Compositional inference via Gibbs sampling that combines particle MCMC, HMC, [Random-Walk MH (RWMH)](https://github.com/TuringLang/AdvancedMH.jl) and [Elliptical Slice Sampling](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/ess.jl)
@@ -20,7 +20,7 @@ Current features include:
 
 Turing's home page, with links to everything you'll need to use Turing is:
 
-https://turing.ml/dev/docs/using-turing/get-started
+https://turinglang.org/dev/docs/using-turing/get-started
 
 
 ## What's changed recently?
@@ -32,7 +32,7 @@ See [releases](https://github.com/TuringLang/Turing.jl/releases).
 Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include [Hong Ge](http://mlg.eng.cam.ac.uk/hong/), [Kai Xu](http://mlg.eng.cam.ac.uk/?portfolio=kai-xu), [Martin Trapp](http://martint.blog), [Mohamed Tarek](https://github.com/mohamed82008), [Cameron Pfiffer](https://business.uoregon.edu/faculty/cameron-pfiffer), [Tor Fjelde](http://retiredparkingguard.com/about.html).
 You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.
 
-Turing is an open source project so if you feel you have some relevant skills and are interested in contributing then please do get in touch. See the [Contributing](https://turing.ml/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github or implementing things yourself and making a pull request. We would also appreciate example models written using Turing.
+Turing is an open source project so if you feel you have some relevant skills and are interested in contributing then please do get in touch. See the [Contributing](https://turinglang.org/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github or implementing things yourself and making a pull request. We would also appreciate example models written using Turing.
 
 ## Issues and Discussions
 Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should can place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not already have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/).