fix readme links broken by MLJ move

README.md

@@ -4,11 +4,11 @@
 
 A package to create bagged homogeneous ensembles of
 machine learning models using the
-[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) machine
+[MLJ](https://JuliaAI.github.io/MLJ.jl/dev/) machine
 learning framework.
 
 For combining models in more general ways, see the [Composing
-Models](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
+Models](https://JuliaAI.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
 section of the MLJ manual.
 
 
@@ -34,16 +34,16 @@ using MLJBase, MLJEnsembles
 In this case you will also need to load code defining an atomic model
 to ensemble. The easiest way to do this is run `Pkg.add("MLJModels");
 using MLJModels` and use the `@load` macro.  See the [Loading Model
-Code](https://alan-turing-institute.github.io/MLJ.jl/dev/loading_model_code/)
+Code](https://JuliaAI.github.io/MLJ.jl/dev/loading_model_code/)
 of the MLJ manual for this and other possibilities.
 
 
 ## Sample usage
 
-See [Data Science Tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/ensembles/).
+See [Data Science Tutorials](https://JuliaAI.github.io/DataScienceTutorials.jl/getting-started/ensembles/).
 
 
 ## Documentation
 
-See the [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/homogeneous_ensembles/#Homogeneous-Ensembles).
+See the [MLJ manual](https://JuliaAI.github.io/MLJ.jl/dev/homogeneous_ensembles/#Homogeneous-Ensembles).