Add details about the dataset in the exercises in library_sklearn.ipynb

library_sklearn.ipynb

@@ -346,8 +346,8 @@
    "source": [
     "#### Exercises on dimensionality reduction\n",
     "\n",
-    "1. Write a function that receives as input a list with already scaled features and targets and returns the indices of the best 5 features using ```SelectKBest``` with mutual information (classification) as scoring function.\n",
-    "2. Write a function that receives as input a list with already scaled features and targets and computes Principal Component Analysis with 5 components and returns the total explained variance ratio. Use ```random_state = 42```."
+    "1. Write a function that receives as input a list with already scaled features and targets (`dataset = [features, targets]`) and returns the indices of the best 5 features using ```SelectKBest``` with mutual information (classification) as scoring function.\n",
+    "2. Write a function that receives as input a list with already scaled features and targets (`dataset = [features, targets]`) and computes Principal Component Analysis with 5 components and returns the total explained variance ratio. Use ```random_state = 42```."
    ]
   },
   {
@@ -444,7 +444,7 @@
    "source": [
     "#### Exercises on unsupervised learning\n",
     "\n",
-    "1. Write a function that receives as input a list with already scaled features and targets and returns the cluster indexes for all samples. Use agglomerative clustering with default parameters."
+    "1. Write a function that receives as input a list with already scaled features and targets (`dataset = [features, targets]`) and returns the cluster indexes for all samples. Use agglomerative clustering with default parameters."
    ]
   },
   {