Updated Docs

docs/src/pca_example.md

@@ -29,11 +29,9 @@ Next, we'll fit a PCA transformation to our image. The reason that we make fitti
 ```julia
 pca_full = fit_transform(PCA, sentinel, method=:cov)
 ```
-```julia
-PCA(in_dim=11, out_dim=11, explained_variance=1.0)
 ```
+PCA(in_dim=11, out_dim=11, explained_variance=1.0)
 
-```
 Projection Matrix:
 11×11 Matrix{Float64}:
  -0.026   -0.2068  -0.359    0.312   -0.0955  -0.4791   0.2245  -0.6117   0.1862  -0.1494   0.1005
@@ -58,11 +56,9 @@ If we look at the cumulative variance, we see that we only need to retain three
 ```julia
 pca = fit_transform(PCA, sentinel, components=3)
 ```
-```julia
-PCA(in_dim=11, out_dim=3, explained_variance=0.989)
 ```
+PCA(in_dim=11, out_dim=3, explained_variance=0.989)
 
-```
 Projection Matrix:
 11×3 Matrix{Float64}:
  -0.026   -0.2068  -0.359