diff --git a/ml4/2_5_1_Exercise.ipynb b/ml4/2_5_1_Exercise.ipynb
index 3b6cb59..9b02dc9 100644
--- a/ml4/2_5_1_Exercise.ipynb
+++ b/ml4/2_5_1_Exercise.ipynb
@@ -56,7 +56,7 @@
    "metadata": {},
    "source": [
     "# Genetic Algorithms\n",
-    "In this section we are going to use the library DEAP [References](#References) for implementing a genetic algorithms.\n",
+    "In this section we are going to use the library DEAP [[References](#References)] for implementing a genetic algorithms.\n",
     "\n",
     "We are going to implement the OneMax problem as seen in class.\n",
     "\n",
@@ -200,11 +200,13 @@
    "source": [
     "## Optimizing ML hyperparameters\n",
     "\n",
-    "One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously we have used GridSearch from Scikit. Using (sklearn-deap)[#References], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
+    "One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously we have used GridSearch from Scikit. Using (sklearn-deap)[[References](#References)], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
     "\n",
     "The same exercise (using the digits dataset) can be found in this [notebook](https://github.com/rsteca/sklearn-deap/blob/master/test.ipynb).\n",
     "\n",
-    "Submit a notebook where you include well-crafted conclusions about the exercises, discussing the pros and cons of using genetic algorithms for this purpose.\n"
+    "Submit a notebook where you include well-crafted conclusions about the exercises, discussing the pros and cons of using genetic algorithms for this purpose.\n",
+    "\n",
+    "Note: There is a problem with the version 0.24 of scikit. Just comment the different approaches."
    ]
   },
   {
@@ -261,6 +263,15 @@
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+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
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@@ -276,7 +287,7 @@
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    "nbconvert_exporter": "python",
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