diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index f0ae17f..88acf02 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -316,13 +316,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**This is the first code example in chapter 5:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -472,13 +465,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Here is second code example in the chapter:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -538,13 +524,6 @@
     "## Polynomial Kernel"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Next code example:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -553,10 +532,8 @@
    "source": [
     "from sklearn.svm import SVC\n",
     "\n",
-    "poly_kernel_svm_clf = make_pipeline(\n",
-    "    StandardScaler(),\n",
-    "    SVC(kernel=\"poly\", degree=3, coef0=1, C=5)\n",
-    ")\n",
+    "poly_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
+    "                                    SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
     "poly_kernel_svm_clf.fit(X, y)"
    ]
   },
@@ -677,23 +654,14 @@
     "## Gaussian RBF Kernel"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Next code example:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "rbf_kernel_svm_clf = make_pipeline(\n",
-    "    StandardScaler(),\n",
-    "    SVC(kernel=\"rbf\", gamma=5, C=0.001)\n",
-    ")\n",
+    "rbf_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
+    "                                   SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
     "rbf_kernel_svm_clf.fit(X, y)"
    ]
   },
@@ -751,29 +719,14 @@
    "execution_count": 20,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# not in the book – this code generates a simple linear dataset\n",
-    "np.random.seed(42)\n",
-    "m = 50\n",
-    "X = 2 * np.random.rand(m, 1)\n",
-    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Next code example:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "from sklearn.svm import LinearSVR\n",
     "\n",
+    "# not in the book – these 3 lines generate a simple linear dataset\n",
+    "np.random.seed(42)\n",
+    "X = 2 * np.random.rand(50, 1)\n",
+    "y = 4 + 3 * X[:, 0] + np.random.randn(50)\n",
+    "\n",
     "svm_reg = make_pipeline(StandardScaler(),\n",
     "                        LinearSVR(epsilon=0.5, random_state=42))\n",
     "svm_reg.fit(X, y)"
@@ -781,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -839,32 +792,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# not in the book – this code generates a simple quadratic dataset\n",
-    "np.random.seed(42)\n",
-    "m = 50\n",
-    "X = 2 * np.random.rand(m, 1) - 1\n",
-    "y = (0.2 + 0.1 * X + 0.5 * X ** 2 + np.random.randn(m, 1) / 10).ravel()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Next code example:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.svm import SVR\n",
     "\n",
+    "# not in the book – these 3 lines generate a simple quadratic dataset\n",
+    "np.random.seed(42)\n",
+    "X = 2 * np.random.rand(50, 1) - 1\n",
+    "y = 0.2 + 0.1 * X[:, 0] + 0.5 * X[:, 0] ** 2 + np.random.randn(50) / 10\n",
+    "\n",
     "svm_poly_reg = make_pipeline(StandardScaler(),\n",
     "                             SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1))\n",
     "svm_poly_reg.fit(X, y)"
@@ -872,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -913,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -962,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1010,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1020,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1081,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1094,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1108,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1117,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1159,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {
     "scrolled": true
    },
@@ -1237,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1264,7 +1202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1292,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1351,7 +1289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1362,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1371,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1383,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1392,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1408,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1425,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1444,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1464,7 +1402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1482,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1507,7 +1445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1524,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1559,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1596,7 +1534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1616,7 +1554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1642,7 +1580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1661,7 +1599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1679,7 +1617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1699,7 +1637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1715,7 +1653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1736,7 +1674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1745,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1762,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [