Merge pull request #958 from svilupp/fix-mini-typos-in-docs

Fix mini typos in docs

README.md

@@ -47,7 +47,7 @@ Science Investment
 Fund](https://www.mbie.govt.nz/science-and-technology/science-and-innovation/funding-information-and-opportunities/investment-funds/strategic-science-investment-fund/ssif-funded-programmes/university-of-auckland/)
 awarded to the University of Auckland.
 
-MLJ been developed with the support of the following organizations:
+MLJ has been developed with the support of the following organizations:
 
 <div align="center">
     <img src="material/Turing_logo.png" width = 100/>
@@ -60,7 +60,7 @@ MLJ been developed with the support of the following organizations:
 
 ### The MLJ Universe
 
-The functionality of MLJ is distributed over a number of repositories
+The functionality of MLJ is distributed over several repositories
 illustrated in the dependency chart below. These repositories live at
 the [JuliaAI](https://github.com/JuliaAI) umbrella organization.
 

ROADMAP.md

@@ -43,7 +43,7 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 ### Adding models
 
 - [ ] **Integrate deep learning** using [Flux.jl](https://github.com/FluxML/Flux.jl.git) deep learning.  [Done](https://github.com/FluxML/MLJFlux.jl) but can
-  improve experience by:
+  improve the experience by:
 
   - [x] finishing iterative model wrapper [#139](https://github.com/alan-turing-institute/MLJ.jl/issues/139)
 
@@ -86,7 +86,7 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 	  [done](https://github.com/JuliaAI/MLJTuning.jl/pull/96)
 
 	- [ ] Bayesian methods, starting with Gaussian Process methods a
-	  la PyMC3. Some preliminary research done .
+	  la PyMC3. Some preliminary research done.
 
 	- [ ] POC for AD-powered gradient descent [#74](https://github.com/alan-turing-institute/MLJ.jl/issues/74)
 
@@ -99,36 +99,36 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 	- [ ] Genetic algorithms
 [#38](https://github.com/alan-turing-institute/MLJ.jl/issues/38)
 
-	- [ ] Particle Swarm Optization (current WIP, GSoC project @lhnguyen-vn)
+	- [ ] Particle Swarm Optimization (current WIP, GSoC project @lhnguyen-vn)
 
 	- [ ] tuning strategies for non-Cartesian spaces of models [MLJTuning
 	#18](https://github.com/JuliaAI/MLJTuning.jl/issues/18), architecture search, and other AutoML workflows
 
 - [ ]  Systematic benchmarking, probably modeled on
 	[MLaut](https://arxiv.org/abs/1901.03678) [#69](https://github.com/alan-turing-institute/MLJ.jl/issues/74)
 
-- [ ]   Give `EnsembleModel` more extendible API and extend beyond bagging
-	(boosting, etc) and migrate to separate repository?
+- [ ]   Give `EnsembleModel` a more extendible API and extend beyond bagging
+	(boosting, etc) and migrate to a separate repository?
 	[#363](https://github.com/alan-turing-institute/MLJ.jl/issues/363)
 
-- [ ]  **†** Enhance complex model compostition:
+- [ ]  **†** Enhance complex model composition:
 
 	- [x] Introduce a canned
 	stacking model wrapper ([POC](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/stacking/)). WIP @olivierlabayle
 
 	- [ ] Get rid of macros for creating pipelines and possibly
-	implement target transforms as wrapper ([MLJBase
+	implement target transforms as wrappers ([MLJBase
 	#594](https://github.com/alan-turing-institute/MLJ.jl/issues/594))
 	WIP @CameronBieganek and @ablaom
 
 
 ### Broadening scope
 
-- [ ] Integrate causal and counterfactual methods for, example,
+- [ ] Integrate causal and counterfactual methods for example,
   applications to FAIRness; see [this
   proposal](https://julialang.org/jsoc/gsoc/MLJ/#causal_and_counterfactual_methods_for_fairness_in_machine_learning)
 
-- [ ] Explore possibility of closer integration of Interpretable
+- [ ] Explore the possibility of closer integration of Interpretable
   Machine Learning approaches, such as Shapley values and lime; see
   [Shapley.jl](https://gitlab.com/ExpandingMan/Shapley.jl),
   [ShapML.jl](https://github.com/nredell/ShapML.jl),
@@ -146,7 +146,7 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 - [ ] Add sparse data support and better support for NLP models; we
 	could use [NaiveBayes.jl](https://github.com/dfdx/NaiveBayes.jl)
 	as a POC (currently wrapped only for dense input) but the API
-	needs finalizing first
+	needs to be finalized first
 	{#731](https://github.com/alan-turing-institute/MLJ.jl/issues/731). Probably
 	need a new SparseTables.jl package.
 
@@ -159,27 +159,27 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 	first, and someone to finish [PR on time series
 	CV](https://github.com/JuliaAI/MLJBase.jl/pull/331). See also [this proposal](https://julialang.org/jsoc/gsoc/MLJ/#time_series_forecasting_at_scale_-_speed_up_via_julia)
 
-- [ ]   Add tools or separate repository for visualization in MLJ.
+- [ ]   Add tools or a separate repository for visualization in MLJ.
 
 	- [x] Extend visualization of tuning plots beyond two-parameters
 	[#85](https://github.com/alan-turing-institute/MLJ.jl/issues/85)
 	(closed).
 	[#416](https://github.com/alan-turing-institute/MLJ.jl/issues/416)
 	[Done](https://github.com/JuliaAI/MLJTuning.jl/pull/121) but might be worth adding alternatives suggested in issue.
 
-	- [ ] visualizing decision boundaries ? [#342](https://github.com/alan-turing-institute/MLJ.jl/issues/342)
+	- [ ] visualizing decision boundaries? [#342](https://github.com/alan-turing-institute/MLJ.jl/issues/342)
 
 	- [ ] provide visualizations that MLR3 provides via [mlr3viz](https://github.com/mlr-org/mlr3viz)
 
-- [ ] Extend API to accomodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/alan-turing-institute/MLJ.jl/issues/780) WIP @davn and @ablaom
+- [ ] Extend API to accommodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/alan-turing-institute/MLJ.jl/issues/780) WIP @davn and @ablaom
 
 - [ ] Add more pre-processing tools:
 
-  - [x] missing value imputation using Gaussina Mixture Model. Done,
+  - [x] missing value imputation using Gaussian Mixture Model. Done,
 	via addition of BetaML model, `MissingImputator`.
 
   - [ ] improve `autotype` method (from ScientificTypes), perhaps by
-	training on large collection of datasets with manually labelled
+	training on a large collection of datasets with manually labelled
 	scitype schema.
 
 - [ ] Add integration with [MLFlow](https://julialang.org/jsoc/gsoc/MLJ/#mlj_and_mlflow_integration); see [this proposal](https://julialang.org/jsoc/gsoc/MLJ/#mlj_and_mlflow_integration)

docs/make.jl

@@ -43,7 +43,7 @@ pages = [
     "Learning Curves" => "learning_curves.md",
     "Preparing Data" => "preparing_data.md",
     "Transformers and Other Unsupervised models" => "transformers.md",
-    "More on Probablistic Predictors" => "more_on_probabilistic_predictors.md",
+    "More on Probabilistic Predictors" => "more_on_probabilistic_predictors.md",
     "Composing Models" => "composing_models.md",
     "Linear Pipelines" => "linear_pipelines.md",
     "Target Transformations" => "target_transformations.md",

docs/src/about_mlj.md

@@ -7,7 +7,7 @@ models](@ref model_list) written in Julia and other languages. In
 particular MLJ wraps a large number of
 [scikit-learn](https://scikit-learn.org/stable/) models.
 
-MLJ is released under the MIT licensed.
+MLJ is released under the MIT license.
 
 ## Lightning tour
 
@@ -47,7 +47,7 @@ booster = Booster(max_depth=2)   # specify hyper-parameter at construction
 booster.nrounds=50               # or mutate afterwards
 ```
 
-This model is an example of an iterative model. As is stands, the
+This model is an example of an iterative model. As it stands, the
 number of iterations `nrounds` is fixed.
 
 
@@ -146,7 +146,7 @@ Extract:
 * Data agnostic, train models on any data supported by the
   [Tables.jl](https://github.com/JuliaData/Tables.jl) interface.
 
-* Extensive, state-of-the art, support for model composition
+* Extensive, state-of-the-art, support for model composition
   (*pipelines*, *stacks* and, more generally, *learning networks*). See more
   [below](#model-composability).
 
@@ -156,7 +156,7 @@ Extract:
 
 * Extensible [tuning
   interface](https://github.com/JuliaAI/MLJTuning.jl),
-  to support growing number of optimization strategies, and designed
+  to support a growing number of optimization strategies, and designed
   to play well with model composition.
 
 * Options to accelerate model evaluation and tuning with
@@ -225,7 +225,7 @@ See also, [Known Issues](@ref)
 
 ## Installation
 
-Initially it is recommended that MLJ and associated packages be
+Initially, it is recommended that MLJ and associated packages be
 installed in a new
 [environment](https://julialang.github.io/Pkg.jl/v1/environments/) to
 avoid package conflicts. You can do this with
@@ -247,7 +247,7 @@ julia> Pkg.test("MLJ")
 ```
 
 It is important to note that MLJ is essentially a big wrapper
-providing unified access to _model providing packages_. For this
+providing unified access to _model-providing packages_. For this
 reason, one generally needs to add further packages to your
 environment to make model-specific code available. This
 happens automatically when you use MLJ's interactive load command
@@ -267,12 +267,12 @@ module or function) see [Loading Model Code](@ref).
 It is recommended that you start with models from more mature
 packages such as DecisionTree.jl, ScikitLearn.jl or XGBoost.jl.
 
-MLJ is supported by a number of satellite packages (MLJTuning,
+MLJ is supported by several satellite packages (MLJTuning,
 MLJModelInterface, etc) which the general user is *not* required to
 install directly. Developers can learn more about these
 [here](https://github.com/alan-turing-institute/MLJ.jl/blob/master/ORGANIZATION.md).
 
-See also the alternative instalation instructions for [Modifying Behavior](@ref).
+See also the alternative installation instructions for [Modifying Behavior](@ref).
 
 
 ## Funding