diff --git a/ml1/2_6_Model_Tuning.ipynb b/ml1/2_6_Model_Tuning.ipynb
index c9c9fd7..49d6333 100644
--- a/ml1/2_6_Model_Tuning.ipynb
+++ b/ml1/2_6_Model_Tuning.ipynb
@@ -314,7 +314,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n",
+    "Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the hyperparameters as an *optimization problem*. \n",
     "\n",
     "The sklearn comes with several optimization techniques for this purpose, such as  **grid search** and  **randomized search**. In this notebook we are going to introduce the former one."
    ]
@@ -323,7 +323,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The sklearn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. "
+    "The sklearn provides an object that, given data, computes the score during the fit of an estimator on a hyperparameter grid and chooses the hyperparameters to maximize the cross-validation score. "
    ]
   },
   {
@@ -371,7 +371,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now evaluate the KFold with this optimized parameter as follows."
+    "We can now evaluate the KFold with this optimized hyperparameter as follows."
    ]
   },
   {
@@ -431,7 +431,7 @@
     "scores = ['precision', 'recall']\n",
     "\n",
     "for score in scores:\n",
-    "    print(\"# Tuning hyper-hyperparameters for %s\" % score)\n",
+    "    print(\"# Tuning hyperparameters for %s\" % score)\n",
     "    print()\n",
     "\n",
     "    if score == 'precision':\n",