minor fixes

Author: ablaom

docs/src/fit_update.md

@@ -43,6 +43,8 @@ model = update(model; n=150)
 predict(model, Distribution(), X)
 ```
 
+See also [Classification](@ref) and [Regression](@ref).
+
 ### Tranformers
 
 A dimension-reducing transformer, `algorithm`  might be used in this way:
@@ -74,14 +76,16 @@ labels = predict(algorithm, X)
 LearnAPI.extras(model)
 ```
 
+See also [Static Algorithms](@ref)
+
 ### Density estimation
 
 In density estimation, `fit` consumes no features, only a target variable; `predict`,
 which consumes no data, returns the learned density:
 
 ```julia
 model = fit(algorithm, y) # no features
-predict(model)            # shortcut for  `predict(model, Distribution())`
+predict(model)  # shortcut for  `predict(model, Distribution())`, or similar
 ```
 
 A one-liner will typically be implemented as well:
@@ -90,6 +94,9 @@ A one-liner will typically be implemented as well:
 predict(algorithm, y)
 ```
 
+See also [Density Estimation](@ref).
+
+
 ## Implementation guide
 
 ### Training

src/fit_update.jl

@@ -37,7 +37,7 @@ overloaded to return `true`.
 The signature must include `verbosity`.
 
 If `data` encapsulates a *target* variable, as defined in LearnAPI.jl documentation, then
-[`LearnAPI.target(data)`] must be overloaded to return it. If [`predict`](@ref) or
+[`LearnAPI.target(data)`](@ref) must be overloaded to return it. If [`predict`](@ref) or
 [`transform`](@ref) are implemented and consume data, then
 [`LearnAPI.features(data)`](@ref) must return something that can be passed as data to
 these methods. A fallback returns `first(data)` if `data` is a tuple, and `data`