diff --git a/docs/user/advanced_examples/PoissonRegression.ipynb b/docs/user/advanced_examples/PoissonRegression.ipynb
index 3bcb14680..a8001447a 100644
--- a/docs/user/advanced_examples/PoissonRegression.ipynb
+++ b/docs/user/advanced_examples/PoissonRegression.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Poisson Regression\n",
     "\n",
-    "This tutorial shows how to train several Generalized Linear Models (GLM) with scikit-learn, quantize them and run them in FHE using Concrete Numpy. We make use of strong quantization to insure the accumulator of the linear part does not overflow when computing in FHE (7-bit accumulator). We show that conversion to FHE does not degrade performance with respect to the quantized model working on values in the clear."
+    "This tutorial shows how to train several Generalized Linear Models (GLM) with scikit-learn, quantize them and run them in FHE using Concrete Numpy. We make use of strong quantization to ensure the accumulator of the linear part does not overflow when computing in FHE (7-bit accumulator). We show that conversion to FHE does not degrade performance with respect to the quantized model working on values in the clear."
    ]
   },
   {
@@ -68,9 +68,9 @@
    "source": [
     "### Insurance claims dataset\n",
     "\n",
-    "In this tutorial we show how to build a regression model that predicts the frequency of incidents in an insurance setting.\n",
+    "In this tutorial, we show how to build a regression model that predicts the frequency of incidents in an insurance setting.\n",
     "\n",
-    "We download a data set from OpenML that contains 670 000 examples giving the frequency of car accidents for drivers of various ages, past accident history, car type, car color, geographical region, etc. We take only the first 50 000 examples to speed up training.\n"
+    "We download a data set from OpenML that contains 670,000 examples giving the frequency of car accidents for drivers of various ages, past accident history, car type, car color, geographical region, etc. We take only the first 50 000 examples to speed up training.\n"
    ]
   },
   {
@@ -870,9 +870,9 @@
    "source": [
     "### Conclusion\n",
     "\n",
-    "In this tutorial we have discussed how we can use Concrete Numpy to convert a scikit-learn based Poisson regression model to FHE. \n",
+    "In this tutorial, we have discussed how we can use Concrete Numpy to convert a scikit-learn based Poisson regression model to FHE. \n",
     "\n",
-    "First of all, we have shown that, with the proper choice of pipeline and parameters, we can do the conversion with little loss of precision. This decrease in the quality of prediction is due to quantization of model weights and input data, and some minor noise can appear due to FHE. This noise is visible on the single variable FHE trend line as minor deviations of the blue curve with respect to the red one. \n",
+    "First of all, we have shown that with the proper choice of pipeline and parameters, we can do the conversion with little loss of precision. This decrease in the quality of prediction is due to quantization of model weights and input data, and some minor noise can appear due to FHE. This noise is visible on the single variable FHE trend line as minor deviations of the blue curve with respect to the red one. \n",
     "\n",
     "Finally, we have shown how conversion of a model to FHE can be done with a single line of code and how quantization is aided by the tools in Concrete Numpy. \n"
    ]