diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index cac126c8c..62e20e649 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -38,6 +38,17 @@
     "assert sys.version_info >= (3, 5)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -210,20 +221,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"fundamentals\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)"
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
   {
@@ -235,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +447,7 @@
        "[2 rows x 24 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -463,7 +476,7 @@
        "Name: Life satisfaction, dtype: float64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -488,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -569,7 +582,7 @@
        "Albania            3995.383                 2010.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1958,7 +1971,7 @@
        "[36 rows x 30 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1971,7 +1984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1982,7 +1995,7 @@
        "Name: United States, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1993,7 +2006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2006,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -2051,7 +2064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2060,7 +2073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2133,7 +2146,7 @@
        "United States       55805.204                7.2"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2144,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2188,7 +2201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2197,7 +2210,7 @@
        "(4.853052800266436, 4.911544589158484e-05)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2214,7 +2227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2250,7 +2263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2266,7 +2279,7 @@
        "5.96244744318815"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2280,7 +2293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2319,7 +2332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2380,7 +2393,7 @@
        "Spain          25864.721                6.5"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2391,7 +2404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2400,7 +2413,7 @@
        "5.766666666666667"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2411,7 +2424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2432,7 +2445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2489,7 +2502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2498,7 +2511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2583,7 +2596,7 @@
        "Luxembourg          101994.093                6.9"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2594,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2611,7 +2624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -2662,7 +2675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -2716,7 +2729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2730,7 +2743,7 @@
        "Name: Life satisfaction, dtype: float64"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2741,7 +2754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2858,7 +2871,7 @@
        "Norway            74822.106                 2015.0  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2869,7 +2882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -2920,7 +2933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2932,7 +2945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2943,7 +2956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2954,7 +2967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 1590eff5a..6db6ed514 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -22,7 +22,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
+    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
    ]
   },
   {
@@ -35,6 +35,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -54,6 +58,7 @@
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"end_to_end_project\"\n",
     "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
     "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
     "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
@@ -67,23 +72,6 @@
     "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/03_classification.ipynb b/03_classification.ipynb
index 2e9d136cf..66f0791e3 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -20,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
+    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
    ]
   },
   {
@@ -33,6 +33,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -51,30 +55,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"classification\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
   {
@@ -164,7 +153,7 @@
     }
    ],
    "source": [
-    "28*28"
+    "28 * 28"
    ]
   },
   {
diff --git a/04_training_linear_models.ipynb b/04_training_linear_models.ipynb
index 21dccaed5..c8dd4b11a 100644
--- a/04_training_linear_models.ipynb
+++ b/04_training_linear_models.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
+    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
    ]
   },
   {
@@ -38,6 +38,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -56,36 +60,21 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"training_linear_models\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",
     "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index 6d8fc30c3..0426f8687 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
+    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
    ]
   },
   {
@@ -40,6 +40,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -58,30 +62,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"svm\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
   {