diff --git a/02_classification.ipynb b/02_classification.ipynb
index 06bd63bcfee78f4f27a21595cd8f080b41562fb6..268af885bf932631e5c29411ce299315eaa4b32d 100644
--- a/02_classification.ipynb
+++ b/02_classification.ipynb
@@ -700,7 +700,7 @@
     "     weight_x * x + weight_y * y = threshold\n",
     "     \n",
     "\n",
-    "can be rearranged to the form `y = m * x  n` and thus defines a line in 2D space. Points fulfilling\n",
+    "can be rearranged to the form `y = a * x + b` and thus defines a line in 2D space. Points fulfilling\n",
     "\n",
     "     weight_x * x + weight_y * y < threshold\n",
     "      \n",
@@ -721,7 +721,7 @@
     "\n",
     "We \n",
     "\n",
-    "1. define a decision line (weights and threshold),\n",
+    "1. define a decision line (using weights and threshold),\n",
     "2. create random 2D samples,\n",
     "3. compute scores for the samples,\n",
     "4. split points according to their score compared to the threshold,\n",
@@ -809,7 +809,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
diff --git a/03_overfitting_and_cross_validation.ipynb b/03_overfitting_and_cross_validation.ipynb
index 2fc879894b49d69ab11f3e1378bf916a2248a050..ddc615d7abbe6e428d40b8918667b092c90f6600 100644
--- a/03_overfitting_and_cross_validation.ipynb
+++ b/03_overfitting_and_cross_validation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -554,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -717,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -726,7 +726,7 @@
        "(300, 5)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -802,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -842,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {
     "tags": [
      "solution"
@@ -853,11 +853,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2-fold accuracy score is 0.840 +/- 0.019\n",
-      "5-fold accuracy score is 0.844 +/- 0.045\n",
-      "10-fold accuracy score is 0.851 +/- 0.073\n",
-      "25-fold accuracy score is 0.857 +/- 0.106\n",
-      "50-fold accuracy score is 0.858 +/- 0.148\n",
+      "  2-fold accuracy score is 0.840 +/- 0.019\n",
+      "  5-fold accuracy score is 0.844 +/- 0.045\n",
+      " 10-fold accuracy score is 0.851 +/- 0.073\n",
+      " 25-fold accuracy score is 0.857 +/- 0.106\n",
+      " 50-fold accuracy score is 0.858 +/- 0.148\n",
       "150-fold accuracy score is 0.859 +/- 0.241\n"
      ]
     }
@@ -884,7 +884,7 @@
     "    scores = cross_val_score(classifier, features, labels, scoring=\"accuracy\", cv=k)\n",
     "    m = scores.mean()\n",
     "    s = scores.std()\n",
-    "    print(\"{:d}-fold accuracy score is {:.3f} +/- {:.3f}\".format(k, m, s))\n",
+    "    print(\"{:3d}-fold accuracy score is {:.3f} +/- {:.3f}\".format(k, m, s))\n",
     "\n",
     "#\n",
     "# Q: What happens with the score?\n",
@@ -919,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "tags": [
      "solution"
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1218,7 +1218,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Moreover, we introduce use of explicit speficiation of a cross-validation method: `StratifiedKFold` from `sklearn.model_selection`. This allows us to spilt data during cross validation in the same way as we did with `train_test_split`, i.e. a) with data shufflling before split, and b) perserving class-proportions of samples, "
+    "Moreover, we introduce use of explicit speficiation of a cross-validation method: `StratifiedKFold` from `sklearn.model_selection`. \n",
+    "\n",
+    "This allows us to spilt data during cross validation in the same way as we did with `train_test_split`, i.e. \n",
+    "\n",
+    "a) with data shufflling before split, and \n",
+    "\n",
+    "b) perserving class-proportions of samples, "
    ]
   },
   {