docstring fixes

examples/svm/plot_svm_scale_c.py

@@ -27,7 +27,7 @@ increase as we add more samples. The penalization term, however, will not
 increase.
 
 When using, for example, :ref:`cross validation <cross_validation>`, to
-set amount of regularization with `C`, there will be a different
+set the amount of regularization with `C`, there will be a different
 amount of samples between every problem that we are using for model
 selection, as well as for the final problem that we want to use for
 training.
@@ -38,7 +38,7 @@ The question that arises is `How do we optimally adjust C to
 account for the different training samples?`
 
 The figures below are used to illustrate the effect of scaling our
-`C` to compensate for the change in the amount of samples, in the
+`C` to compensate for the change in the number of samples, in the
 case of using an `L1` penalty, as well as the `L2` penalty.
 
 L1-penalty case
@@ -47,7 +47,7 @@ In the `L1` case, theory says that prediction consistency
 (i.e. that under given hypothesis, the estimator
 learned predicts as well as an model knowing the true distribution)
 is not possible because of the bias of the `L1`. It does say, however,
-that model consistancy, in terms of finding the right set of non-zero
+that model consistency, in terms of finding the right set of non-zero
 parameters as well as their signs, can be achieved by scaling
 `C1`.
 
@@ -64,7 +64,7 @@ corresponding cross-validation scores on the `y-axis`, for several different
 fractions of a generated data-set.
 
 In the `L1` penalty case, the results are best when scaling our `C` with
-the amount of samples, `n`, which can be seen in the first figure.
+the number of samples, `n`, which can be seen in the first figure.
 
 For the `L2` penalty case, the best result comes from the case where `C`
 is not scaled.