diff --git a/02_classification.ipynb b/02_classification.ipynb
index 62cbbd3cb1276c3b510ce76ad6ccfcf3dc9a936b..cf21a234e47b2b2f81bfee649a526bdd2c40fd66 100644
--- a/02_classification.ipynb
+++ b/02_classification.ipynb
@@ -931,7 +931,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Feature engineering\n",
+    "## But.. what if there are more than two classes?\n",
+    "\n",
+    "\n",
+    "Previous and the following examples in this script consider two class problems.\n",
+    "Before we dig deeper into classification, let's say a few words on how to handle more than two classes.\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\"><p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "    The general idea for <code>n > 2</code> classes is to build multiple 2-class classifiers and determine a winning class by applying all of them:\n",
+    "<ul>\n",
+    "    <li>in the <strong>one-vs-all</strong> approach build <code>n</code> classifiers for \"label n vs. the rest\";</li>\n",
+    "    <li>in the <strong>one-vs-one</strong> approach builds  classifiers for `label i vs label j` (in total <code>n x (n - 1) / 2</code> classifiers).</li>\n",
+    "</ul>\n",
+    "</p></div>\n",
+    "\n",
+    "For new incoming data then all classifiers (`n` or `n x (n -1) / 2`) are applied and the overall winning class gives the final result.\n",
+    "\n",
+    "For instance, to classify images of digits:\n",
+    "\n",
+    "- we could build 10 classifiers `is it 0 or other digit`, `is it 1 or other digit`, etc.\n",
+    "  \n",
+    "  A new image then would hopefully yield `True` for exactly one of the classifier, in other situations the result is unclear.\n",
+    "   \n",
+    "   \n",
+    "- we could build 45 classifiers `is it 0 or 1`, `is it 0 or 2`, etc.\n",
+    "\n",
+    "  For a new image we could choose the final outcome based on which class \"wins\" most often.\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
+    "    In <code>scikit-learn</code> many classifiers support multi-class problems out of the box and also offer functionalities to implement <strong>one-vs-all</strong> or <strong>one-vs-one</strong> in some cases (cf. <a href=\"https://scikit-learn.org/stable/modules/multiclass.html\"><code>scikit-learn</code> multiclass and multilabel algorithms</a>).\n",
+    "</p></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature engineering\n",
     "\n",
     "To improve ML performance we can try to create new feature by transformation of existing features. This process is called **feature engineering**."
    ]
@@ -940,7 +978,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### An example for feature engineering"
+    "### An example for feature engineering"
    ]
   },
   {
@@ -990,7 +1028,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Another example for feature engineering"
+    "### Another example for feature engineering"
    ]
   },
   {
@@ -1220,7 +1258,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Engineer polynomial features using `scikit-learn`\n",
+    "### Engineer polynomial features using `scikit-learn`\n",
     "\n",
     "*Polynomial features* are a way to (semi-)automatically engineere new non-linear features. These are all polynomial combinations of the features (up to given degree)."
    ]
@@ -1819,44 +1857,6 @@
     "    print()\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Side note: what if there are more than two classes?\n",
-    "\n",
-    "\n",
-    "Previous and the following examples in this script consider two class problems.\n",
-    "Before we dig deeper into classification, let's say a few words on how to handle more than two classes.\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\"><p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "    The general idea for <code>n > 2</code> classes is to build multiple 2-class classifiers and determine a winning class by applying all of them:\n",
-    "<ul>\n",
-    "    <li>in the <strong>one-vs-all</strong> approach build <code>n</code> classifiers for \"label n vs. the rest\";</li>\n",
-    "    <li>in the <strong>one-vs-one</strong> approach builds  classifiers for `label i vs label j` (in total <code>n x (n - 1) / 2</code> classifiers).</li>\n",
-    "</ul>\n",
-    "</p></div>\n",
-    "\n",
-    "For new incoming data then all classifiers (`n` or `n x (n -1) / 2`) are applied and the overall winning class gives the final result.\n",
-    "\n",
-    "For instance, to classify images of digits:\n",
-    "\n",
-    "- we could build 10 classifiers `is it 0 or other digit`, `is it 1 or other digit`, etc.\n",
-    "  \n",
-    "  A new image then would hopefully yield `True` for exactly one of the classifier, in other situations the result is unclear.\n",
-    "   \n",
-    "   \n",
-    "- we could build 45 classifiers `is it 0 or 1`, `is it 0 or 2`, etc.\n",
-    "\n",
-    "  For a new image we could choose the final outcome based on which class \"wins\" most often.\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
-    "    In <code>scikit-learn</code> many classifiers support multi-class problems out of the box and also offer functionalities to implement <strong>one-vs-all</strong> or <strong>one-vs-one</strong> in some cases (cf. <a href=\"https://scikit-learn.org/stable/modules/multiclass.html\"><code>scikit-learn</code> multiclass and multilabel algorithms</a>).\n",
-    "</p></div>"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 23,