diff --git a/src/MLJ.jl b/src/MLJ.jl
index 540310f97..e6ed00429 100644
--- a/src/MLJ.jl
+++ b/src/MLJ.jl
@@ -13,6 +13,7 @@ export @curve, @pcurve, pretty,                   # utilities.jl
     Params, params, set_params!,                  # parameters.jl
     strange, iterator,                            # parameters.jl
     Grid, TunedModel, learning_curve!,            # tuning.jl
+    learning_curve,                               # tuning.jl
     EnsembleModel,                                # ensembles.jl
     rebind!,                                      # networks.jl
     machines, sources, anonymize!,                # composites.jl
diff --git a/src/tuning.jl b/src/tuning.jl
index 6e4eae464..844c7c43f 100644
--- a/src/tuning.jl
+++ b/src/tuning.jl
@@ -469,3 +469,16 @@ function learning_curve!(mach::Machine{<:Supervised};
             parameter_values=parameter_values,
             measurements = measurements_)
 end
+
+"""
+    learning_curve(model::Supervised, args...; kwargs...)
+
+Plot a learning curve (or curves) without first constructing a
+machine. Equivalent to `learing_curve!(machine(model, args...);
+kwargs...)
+
+See [learning_curve!](@ref)
+
+"""
+learning_curve(model::Supervised, args...; kwargs...) =
+    learning_curve!(machine(model, args...); kwargs...)
diff --git a/test/tuning.jl b/test/tuning.jl
index fe759aac2..71d98f034 100644
--- a/test/tuning.jl
+++ b/test/tuning.jl
@@ -191,13 +191,16 @@ end
 
 @testset "learning curves" begin
     atom = FooBarRegressor()
-    ensemble = EnsembleModel(atom=atom, n=500)
+    ensemble = EnsembleModel(atom=atom, n=50, rng=1)
     mach = machine(ensemble, X, y)
-    r_lambda = range(ensemble, :(atom.lambda), lower=0.0001, upper=0.1, scale=:log10)
+    r_lambda = range(ensemble, :(atom.lambda),
+                     lower=0.0001, upper=0.1, scale=:log10)
     curve = MLJ.learning_curve!(mach; range=r_lambda)
     atom.lambda=0.3
     r_n = range(ensemble, :n, lower=10, upper=100)
     curve2 = MLJ.learning_curve!(mach; range=r_n)
+    curve3 = learning_curve(ensemble, X, y; range=r_n)
+    @test curve2.measurements ≈ curve3.measurements
 end
 end # module
 true