update links broken by MLJ move

Author: ablaom

README.md

@@ -2,7 +2,7 @@
 
 A light-weight interface for developers wanting to integrate
 machine learning models into
-[MLJ](https://github.com/alan-turing-institute/MLJ.jl).
+[MLJ](https://github.com/JuliaAI/MLJ.jl).
 
 
 | Linux | Coverage |
@@ -12,8 +12,8 @@ machine learning models into
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliaai.github.io/MLJModelInterface.jl/dev/)
 
 
-[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) is a framework for evaluating,
+[MLJ](https://JuliaAI.github.io/MLJ.jl/dev/) is a framework for evaluating,
 combining and optimizing machine learning models in Julia. A third party package wanting
 to integrate their machine learning models into MLJ must import the module
 `MLJModelInterface` defined in this package, as described in the
-[documentation](https://juliaai.github.io/MLJModelInterface.jl/dev/).
+[documentation](https://JuliaAI.github.io/MLJModelInterface.jl/dev/).

docs/src/index.md

@@ -1,7 +1,7 @@
 # Adding Models for General Use
 
 The machine learning tools provided by
-[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) can be applied to the models in
+[MLJ](https://JuliaAI.github.io/MLJ.jl/dev/) can be applied to the models in
 any package that imports 
 [MLJModelInterface](https://github.com/JuliaAI/MLJModelInterface.jl) and implements the
 API defined there, as outlined in this document. 
@@ -15,7 +15,7 @@ or by a stand-alone "interface-only" package, using the template
 [MLJExampleInterface.jl](https://github.com/JuliaAI/MLJExampleInterface.jl) (see [Where to
 place code implementing new models](@ref) below). For a list of packages implementing the
 MLJ model API (natively, and in interface packages) see
-[here](https://alan-turing-institute.github.io/MLJ.jl/dev/list_of_supported_models/).
+[here](https://JuliaAI.github.io/MLJ.jl/dev/list_of_supported_models/).
 
 ## Important
 
@@ -31,7 +31,7 @@ project's [extras] and [targets]. In testing, simply use `MLJBase` in
 place of `MLJModelInterface`.
 
 It is assumed the reader has read the [Getting
-Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/) section of
+Started](https://JuliaAI.github.io/MLJ.jl/dev/getting_started/) section of
 the MLJ manual.  To implement the API described here, some familiarity with the following
 packages is also helpful:
 
@@ -52,5 +52,5 @@ packages is also helpful:
 In MLJ, the basic interface exposed to the user, built atop the model interface described
 here, is the *machine interface*. After a first reading of this document, the reader may
 wish to refer to [MLJ
-Internals](https://alan-turing-institute.github.io/MLJ.jl/dev/internals/) for context.
+Internals](https://JuliaAI.github.io/MLJ.jl/dev/internals/) for context.
 

docs/src/iterative_models.md

@@ -18,11 +18,11 @@ If an MLJ `Machine` is being `fit!` and it is not the first time, then `update`
 instead of `fit`, unless the machine `fit!` has been called with a new `rows` keyword
 argument. However, `MLJModelInterface` defines a fallback for `update` which just calls
 `fit`. For context, see the
-[Internals](https://alan-turing-institute.github.io/MLJ.jl/dev/internals/) section of the
+[Internals](https://JuliaAI.github.io/MLJ.jl/dev/internals/) section of the
 MLJ manual.
 
 Learning networks wrapped as models constitute one use case (see the [Composing
-Models](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/) section of
+Models](https://JuliaAI.github.io/MLJ.jl/dev/composing_models/) section of
 the MLJ manual): one would like each component model to be retrained only when
 hyperparameter changes "upstream" make this necessary. In this case, MLJ provides a
 fallback (specifically, the fallback is for any subtype of `SupervisedNetwork =

docs/src/quick_start_guide.md

@@ -18,11 +18,11 @@ understanding of how things work with MLJ.  In particular, you are familiar with
 - [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl), if working
   with finite discrete data, e.g., doing classification; see also the [Working with
   Categorical
-  Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/)
+  Data](https://JuliaAI.github.io/MLJ.jl/dev/working_with_categorical_data/)
   section of the MLJ manual.
 
 If you're not familiar with any one of these points, the [Getting
-Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/) section of
+Started](https://JuliaAI.github.io/MLJ.jl/dev/getting_started/) section of
 the MLJ manual may help.
 
 *But tables don't make sense for my model!* If a case can be made that
@@ -273,7 +273,7 @@ implementation creates:
   affect the outcome of training. It is okay to add "control"
   parameters (such as specifying an `acceleration` parameter specifying
   computational resources, as
-  [here](https://github.com/alan-turing-institute/MLJ.jl/blob/master/src/ensembles.jl#L193)).
+  [here](https://github.com/JuliaAI/MLJ.jl/blob/master/src/ensembles.jl#L193)).
 - Use `report` to return *everything else*, including model-specific
   *methods* (or other callable objects). This includes feature rankings,
   decision boundaries, SVM support vectors, clustering centres,

docs/src/serialization.md

@@ -10,7 +10,7 @@ implemented in languages other than Julia.
 
 The MLJ user can serialize and deserialize machines, as she would any other julia
 object. (This user has the option of first removing data from the machine. See the [Saving
-machines](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Saving-machines)
+machines](https://JuliaAI.github.io/MLJ.jl/dev/machines/#Saving-machines)
 section of the MLJ manual for details.) However, a problem can occur if a model's
 `fitresult` (see [The fit method](@ref)) is not a persistent object. For example, it might
 be a C pointer that would have no meaning in a new Julia session.

docs/src/static_models.md

@@ -2,7 +2,7 @@
 
 A model type subtypes `Static <: Unsupervised` if it does not generalize to new data but
 nevertheless has hyperparameters. See the [Static
-transformers](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Static-transformers)
+transformers](https://JuliaAI.github.io/MLJ.jl/dev/transformers/#Static-transformers)
 section of the MLJ manual for examples. In the `Static` case, `transform` can have
 multiple arguments and `input_scitype` refers to the allowed scitype of the slurped data,
 *even if there is only a single argument.* For example, if the signature is

docs/src/trait_declarations.md

@@ -128,7 +128,7 @@ MMI.metadata_model(
 
 *Important.* Do not omit the `load_path` specification. If unsure what
 it should be, post an issue at
-[MLJ](https://github.com/alan-turing-institute/MLJ.jl/issues).
+[MLJ](https://github.com/JuliaAI/MLJ.jl/issues).
 
 ```@docs
 MMI.metadata_pkg

docs/src/type_declarations.md

@@ -56,7 +56,7 @@ of `nothing`.
 ### Hyperparameters for parallelization options
 
 The section [Acceleration and
-Parallelism](https://alan-turing-institute.github.io/MLJ.jl/dev/acceleration_and_parallelism/)
+Parallelism](https://JuliaAI.github.io/MLJ.jl/dev/acceleration_and_parallelism/)
 of the MLJ manual indicates how users specify an option to run an algorithm using
 distributed processing or multithreading. A hyperparameter specifying such an option
 should be called `acceleration`. Its value `a` should satisfy `a isa AbstractResource`
@@ -66,7 +66,7 @@ run on a GPU is ordinarily indicated with the `CUDALibs()` resource.
 ### hyperparameter access and mutation
 
 To support hyperparameter optimization (see the [Tuning
-Models](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/) section of the
+Models](https://JuliaAI.github.io/MLJ.jl/dev/tuning_models/) section of the
 MLJ manual) any hyperparameter to be individually controlled must be:
 
 - property-accessible; nested property access allowed, as in

docs/src/unsupervised_models.md

@@ -17,7 +17,7 @@ similar fashion. The main differences are:
   Static`, in which case there is no restriction.  A use-case for `predict` is K-means
   clustering that `predict`s labels and `transform`s input features into a space of lower
   dimension. See the [Transformers that also
-  predict](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Transformers-that-also-predict)
+  predict](https://JuliaAI.github.io/MLJ.jl/dev/transformers/#Transformers-that-also-predict)
   section of the MLJ manual for an example.
 
 - The `target_scitype` refers to the output of `predict`, if implemented. A new trait,

docs/src/where_to_put_code.md

@@ -12,7 +12,7 @@ to all MLJ users:
    algorithms implementing the interface. An example is
    [`EvoTrees.jl`](https://github.com/Evovest/EvoTrees.jl/blob/master/src/MLJ.jl). In
    this case, it is sufficient to open an issue at
-   [MLJ](https://github.com/alan-turing-institute/MLJ.jl) requesting
+   [MLJ](https://github.com/JuliaAI/MLJ.jl) requesting
    the package to be registered with MLJ. Registering a package allows
    the MLJ user to access its models' metadata and to selectively load
    them.