diff --git a/docs/user/advanced_examples/DecisionTreeClassifier.ipynb b/docs/user/advanced_examples/DecisionTreeClassifier.ipynb
index cfb649411..6300704c7 100644
--- a/docs/user/advanced_examples/DecisionTreeClassifier.ipynb
+++ b/docs/user/advanced_examples/DecisionTreeClassifier.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "Trees are a popular class of algorithm in Machine Learning. In this notebook we build a simple Decision Tree Classifier using `scikit-learn` to show that they can be executed homomorphically using Concrete Numpy.\n",
     "\n",
-    "State of the art classifiers are generally a bit more complex than a single decision tree, here we wanted to demonstrate FHE decision trees so results may not compete with the best models out there!\n",
+    "State of the art classifiers are generally a bit more complex than a single decision tree, but here we wanted to demonstrate FHE decision trees so results may not compete with the best models out there.\n",
     "\n",
     "Converting a tree working over quantized data to its FHE equivalent takes only a few lines of code thanks to Concrete Numpy.\n",
     "\n",
@@ -614,7 +614,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this notebook we showed how to quantize a dataset to train a tree directly on integer data so that it's FHE friendly. We saw that despite quantization and its smaller depth the quantized tree classification capabilities were close to a tree trained on the original real-valued dataset.\n",
+    "In this notebook we showed how to quantize a dataset to train a tree directly on integer data so that it is FHE friendly. We saw that despite quantization and its smaller depth, the quantized tree classification capabilities were close to a tree trained on the original real-valued dataset.\n",
     "\n",
     "We then used the Hummingbird paper's algorithm to transform a tree evaluation to a few tensor operations which can be compiled by the Concrete Numpy to an FHE circuit.\n",
     "\n",