diff --git a/07_regression.ipynb b/07_regression.ipynb
index 89119f0a27f42807abcc83a5eebadd474902a6d0..7c0455ea14452505e5cd1b64f9a2d4e2f123477e 100644
--- a/07_regression.ipynb
+++ b/07_regression.ipynb
@@ -893,7 +893,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Some algorithms from scikit-learn\n",
+    "## Some algorithms from `scikit-learn`\n",
     "\n",
     "- `sklearn.linear_model.LinearRegression` is a linear regression method, which only works well for target values which can be described as a linear combination of feature values. This is also known as linear least squares method.\n",
     "\n",
@@ -910,8 +910,8 @@
     "\n",
     "- `sklearn.tree.DecisionTreeRegressor` expands the concept of decision trees to regression [is documented here](https://scikit-learn.org/stable/modules/tree.html#regression).\n",
     "\n",
-    "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) which might be of interest if your target values are bounded or discrete or if the error distribution is skewed. \n",
-    "   - This is usually the case when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities.  The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression)  provides a formal insight as well as tips for choosing GLM with some use case examples. \n",
+    "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) \n",
+    "   - These models are usually of interest when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities.  The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression)  provides a formal insight as well as tips for choosing GLM with some use case examples. \n",
     "   - Beyond that the [wikipedia article about generalized linear models](https://en.wikipedia.org/wiki/Generalized_linear_model#Intuition) gives a nice intuition and [this discussion](https://stats.stackexchange.com/questions/190763/how-to-decide-which-glm-family-to-use) provides a guide which GLM should be used when.\n",
     "   - For assessing and hyperparameter-optimization of such General Linear Models you should also use [suitable metrics from scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-poisson-gamma-and-tweedie-deviances)."
    ]