Update README.md

README.md

@@ -38,6 +38,7 @@ A lot of machine learning approaches are configurable. This means that there are
 * and the choice of the kernel function.
 
 Now we need to find the best values for our hyperparameters. Implement the hyperparameter search in the function `cv_svm` following these steps:
+
 6.1. Define a dictionary of parameters that you want to cross-validate. (Hint: Reasonable values for $C$ range from 0.01 to 1000, while for kernels it is usually sufficient to test `linear`, `rgb` and `poly`.)
 
 6.2. Initialize your model using the `sklearn.svm.SVC` class. Use the ``sklearn.model_selection.GridSearchCV`` class to find optimal hyperparameters for this task.