diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index 6a080af..8b99fce 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "**Chapter 1 – The Machine Learning landscape**\n",
     "\n",
@@ -14,20 +11,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Setup"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
    ]
@@ -36,9 +27,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "slideshow": {
      "slide_type": "-"
     }
@@ -50,11 +38,10 @@
     "\n",
     "# Common imports\n",
     "import numpy as np\n",
-    "import numpy.random as rnd\n",
     "import os\n",
     "\n",
     "# to make this notebook's output stable across runs\n",
-    "rnd.seed(42)\n",
+    "np.random.seed(42)\n",
     "\n",
     "# To plot pretty figures\n",
     "%matplotlib inline\n",
@@ -73,35 +60,173 @@
     "    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='png', dpi=300)\n",
+    "\n",
+    "# Ignore useless warnings (see SciPy issue #5998)\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "# Load and prepare Life satisfaction data"
+    "# Code example 1-1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function just merges the OECD's life satisfaction data and the IMF's GDP per capita data. It's a bit too long and boring and it's not specific to Machine Learning, which is why I left it out of the book."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
+    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
+    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
+    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
+    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
+    "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
+    "                                  left_index=True, right_index=True)\n",
+    "    full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
+    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
+    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
+    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code in the book expects the data files to be located in the current directory. I just tweaked it here to fetch the files in datasets/lifesat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "datapath = os.path.join(\"datasets\", \"lifesat\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code example\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
+    "import sklearn.linear_model\n",
     "\n",
-    "# Download CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
-    "datapath = \"datasets/lifesat/\"\n",
+    "# Load the data\n",
+    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
+    "gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
+    "                             encoding='latin1', na_values=\"n/a\")\n",
     "\n",
-    "oecd_bli = pd.read_csv(datapath+\"oecd_bli_2015.csv\", thousands=',')\n",
+    "# Prepare the data\n",
+    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
+    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
+    "y = np.c_[country_stats[\"Life satisfaction\"]]\n",
+    "\n",
+    "# Visualize the data\n",
+    "country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
+    "plt.show()\n",
+    "\n",
+    "# Select a linear model\n",
+    "model = sklearn.linear_model.LinearRegression()\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "X_new = [[22587]]  # Cyprus' GDP per capita\n",
+    "print(model.predict(X_new)) # outputs [[ 5.96242338]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Note: you can ignore the rest of this notebook, it just generates many of the figures in chapter 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load and prepare Life satisfaction data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want, you can get fresh data from the OECD's website.\n",
+    "Download the CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
+    "and save it to `datasets/lifesat/`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
     "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
     "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
     "oecd_bli.head(2)"
@@ -109,12 +234,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "oecd_bli[\"Life satisfaction\"].head()"
@@ -122,25 +243,24 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Load and prepare GDP per capita data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like above, you can update the GDP per capita data if you want. Just download data from http://goo.gl/j1MSKe (=> imf.org) and save it to `datasets/lifesat/`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Download data from http://goo.gl/j1MSKe (=> imf.org)\n",
     "gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',', delimiter='\\t',\n",
     "                             encoding='latin1', na_values=\"n/a\")\n",
     "gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
@@ -150,12 +270,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
@@ -165,12 +281,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "full_country_stats[[\"GDP per capita\", 'Life satisfaction']].loc[\"United States\"]"
@@ -178,12 +290,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
@@ -195,12 +303,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
@@ -224,25 +328,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "sample_data.to_csv(\"life_satisfaction_vs_gdp_per_capita.csv\")"
+    "sample_data.to_csv(os.path.join(\"datasets\", \"lifesat\", \"lifesat.csv\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data.loc[list(position_text.keys())]"
@@ -250,12 +346,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -278,12 +370,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn import linear_model\n",
@@ -297,12 +385,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
@@ -317,12 +401,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cyprus_gdp_per_capita = gdp_per_capita.loc[\"Cyprus\"][\"GDP per capita\"]\n",
@@ -333,12 +413,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3), s=1)\n",
@@ -356,12 +432,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data[7:10]"
@@ -369,12 +441,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "(5.1+5.7+6.5)/3"
@@ -382,28 +450,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "backup = oecd_bli, gdp_per_capita\n",
     "\n",
     "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
-    "    return sample_data"
+    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
+    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
+    "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
+    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
+    "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
+    "                                  left_index=True, right_index=True)\n",
+    "    full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
+    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
+    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
+    "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Code example\n",
@@ -440,12 +509,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "oecd_bli, gdp_per_capita = backup"
@@ -453,12 +518,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "missing_data"
@@ -466,12 +527,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [],
    "source": [
     "position_text2 = {\n",
@@ -487,12 +544,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
@@ -522,12 +575,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "full_country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
@@ -550,12 +599,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "full_country_stats.loc[[c for c in full_country_stats.index if \"W\" in c.upper()]][\"Life satisfaction\"]"
@@ -563,12 +608,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "gdp_per_capita.loc[[c for c in gdp_per_capita.index if \"W\" in c.upper()]].head()"
@@ -576,12 +617,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=(8,3))\n",
@@ -611,12 +648,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [],
    "source": [
     "backup = oecd_bli, gdp_per_capita\n",
@@ -627,12 +660,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Replace this linear model:\n",
@@ -641,12 +670,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# with this k-neighbors regression model:\n",
@@ -655,12 +680,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = np.c_[country_stats[\"GDP per capita\"]]\n",
@@ -677,11 +698,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -702,7 +719,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.3"
   },
   "nav_menu": {},
   "toc": {
@@ -723,5 +740,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }