Update 2_5_1_Exercise.ipynb

Added optional exercises.

ml4/2_5_1_Exercise.ipynb

@@ -187,9 +187,9 @@
    "metadata": {},
    "source": [
     "## Comparing\n",
-    "Your task is modify the previous code to canonical GA configuration from Holland (look at the lesson's slides). In addition you should consult the [DEAP API](http://deap.readthedocs.io/en/master/api/tools.html#operators).\n",
+    "Your task is to modify the previous code to canonical GA configuration from Holland (look at the lesson's slides). In addition you should consult the [DEAP API](http://deap.readthedocs.io/en/master/api/tools.html#operators).\n",
     "\n",
-    "Submit your notebook and include a the modified code, and a comparison of the effects of these changes. \n",
+    "Submit your notebook and include a modified code and a comparison of the effects of these changes. \n",
     "\n",
     "Discuss your findings."
    ]
@@ -198,7 +198,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimizing ML hyperparameters\n",
+    "## Optional. 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](#References)], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
     "\n",
@@ -206,7 +206,7 @@
     "\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."
+    "Note: There is a problem with Scikit version 0.24. Comment on the different approaches."
    ]
   },
   {
@@ -222,7 +222,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimizing a ML pipeline with a genetic algorithm\n",
+    "## Optional. Optimizing an ML pipeline with a genetic algorithm\n",
     "\n",
     "The library [TPOT](#References) optimizes ML pipelines and comes with a lot of (examples)[https://epistasislab.github.io/tpot/examples/] and even notebooks, for example for the [iris dataset](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb).\n",
     "\n",