diff --git a/02_classification.ipynb b/02_classification.ipynb
index af4939395a1fbede6da5270bf7d17a0425d2d5ad..613d93a546a24ef7bb5d4ebad38afee6dd2f1825 100644
--- a/02_classification.ipynb
+++ b/02_classification.ipynb
@@ -233,7 +233,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Consequence: A simple classifier could use these scores and use a threshold around 3.5 to assign a class label."
+    "Consequence: A simple classifier could use these scores and use a threshold around 3.5 to assign a class label:"
    ]
   },
   {
@@ -280,7 +280,7 @@
     "\n",
     "Modify the weights in the beer classifiers and check if you can improve separation in the histogram.\n",
     "\n",
-    "In `scikit-lear` the weights of a trained linear are availble via the `coef_` attribute. Extract the weights from the `LogisticRegression` classifier example from the last script and try them."
+    "In `scikit-learn` the weights of a trained linear are availble via the `coef_` attribute. Extract the weights from the `LogisticRegression` classifier example from the last script and try them."
    ]
   },
   {
@@ -289,7 +289,7 @@
    "source": [
     "## Geometrical interpretation of feature vectors\n",
     "\n",
-    "If you take the values of a input-feature vector you can imagine this as a point in a d-dimensional space.\n",
+    "If you take the values of a input-feature vector you can imagine this as a point in a n-dimensional space.\n",
     "\n",
     "\n",
     "E.g. if a data set consists of  feature vectors of length 2, you can interpret the first feature value as a x-coordinate and the second value as a y-coordinate.\n",