diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index e3fd493..96ff4c5 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -70,7 +70,7 @@
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",
-    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
+    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"internal gelsd\")"
    ]
   },
   {
@@ -980,12 +980,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And a transformer to just select a subset of the Pandas DataFrame columns:"
+    "**Warning**: earlier versions of the book applied different transformations to different columns using a solution based on a `DataFrameSelector` transformer and a `FeatureUnion` (see below). It is now preferable to use the `ColumnTransformer` class that will be introduced in Scikit-Learn 0.20. For now we import it from `future_encoders.py`, but when Scikit-Learn 0.20 is released, you can import it from `sklearn.compose` instead:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from future_encoders import ColumnTransformer\n",
+    "\n",
+    "num_attribs = list(housing_num)\n",
+    "cat_attribs = [\"ocean_proximity\"]\n",
+    "\n",
+    "full_pipeline = ColumnTransformer([\n",
+    "        (\"num\", num_pipeline, num_attribs),\n",
+    "        (\"cat\", OneHotEncoder(), cat_attribs),\n",
+    "    ])\n",
+    "\n",
+    "housing_prepared = full_pipeline.fit_transform(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_prepared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_prepared.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For reference, here is the old solution based on a `DataFrameSelector` transformer (to just select a subset of the Pandas `DataFrame` columns), and a `FeatureUnion`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -993,7 +1037,7 @@
     "\n",
     "# Create a class to select numerical or categorical columns \n",
     "# since Scikit-Learn doesn't handle DataFrames yet\n",
-    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
+    "class OldDataFrameSelector(BaseEstimator, TransformerMixin):\n",
     "    def __init__(self, attribute_names):\n",
     "        self.attribute_names = attribute_names\n",
     "    def fit(self, X, y=None):\n",
@@ -1011,57 +1055,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
     "num_attribs = list(housing_num)\n",
     "cat_attribs = [\"ocean_proximity\"]\n",
     "\n",
-    "num_pipeline = Pipeline([\n",
-    "        ('selector', DataFrameSelector(num_attribs)),\n",
+    "old_num_pipeline = Pipeline([\n",
+    "        ('selector', OldDataFrameSelector(num_attribs)),\n",
     "        ('imputer', Imputer(strategy=\"median\")),\n",
     "        ('attribs_adder', CombinedAttributesAdder()),\n",
     "        ('std_scaler', StandardScaler()),\n",
     "    ])\n",
     "\n",
-    "cat_pipeline = Pipeline([\n",
-    "        ('selector', DataFrameSelector(cat_attribs)),\n",
+    "old_cat_pipeline = Pipeline([\n",
+    "        ('selector', OldDataFrameSelector(cat_attribs)),\n",
     "        ('cat_encoder', OneHotEncoder(sparse=False)),\n",
     "    ])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.pipeline import FeatureUnion\n",
     "\n",
-    "full_pipeline = FeatureUnion(transformer_list=[\n",
-    "        (\"num_pipeline\", num_pipeline),\n",
-    "        (\"cat_pipeline\", cat_pipeline),\n",
+    "old_full_pipeline = FeatureUnion(transformer_list=[\n",
+    "        (\"num_pipeline\", old_num_pipeline),\n",
+    "        (\"cat_pipeline\", old_cat_pipeline),\n",
     "    ])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
-    "housing_prepared = full_pipeline.fit_transform(housing)\n",
-    "housing_prepared"
+    "old_housing_prepared = old_full_pipeline.fit_transform(housing)\n",
+    "old_housing_prepared"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is the same as with the `ColumnTransformer`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
-    "housing_prepared.shape"
+    "np.allclose(housing_prepared, old_housing_prepared)"
    ]
   },
   {
@@ -1073,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,11 +1136,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# let's try the full pipeline on a few training instances\n",
+    "# let's try the full preprocessing pipeline on a few training instances\n",
     "some_data = housing.iloc[:5]\n",
     "some_labels = housing_labels.iloc[:5]\n",
     "some_data_prepared = full_pipeline.transform(some_data)\n",
@@ -1106,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1124,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1150,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1162,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1181,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1194,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1208,7 +1259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1220,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1232,7 +1283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1258,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1268,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1284,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1313,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1322,7 +1373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1338,7 +1389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1349,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1358,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1378,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1389,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,12 +1450,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
     "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
-    "cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"]\n",
+    "#cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"] # old solution\n",
+    "cat_encoder = full_pipeline.named_transformers_[\"cat\"]\n",
     "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
     "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
     "sorted(zip(feature_importances, attributes), reverse=True)"
@@ -1412,7 +1464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1430,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1446,7 +1498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1455,7 +1507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1478,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1496,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1521,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1552,7 +1604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1571,7 +1623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1635,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1653,7 +1705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1683,7 +1735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1716,7 +1768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1734,7 +1786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1782,7 +1834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1821,7 +1873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1857,7 +1909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1873,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1883,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1899,7 +1951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1915,7 +1967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1927,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1943,7 +1995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1959,7 +2011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1989,7 +2041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2002,7 +2054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2018,7 +2070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2052,12 +2104,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
     "param_grid = [\n",
-    "        {'preparation__num_pipeline__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
+    "        {'preparation__num__imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
     "         'feature_selection__k': list(range(1, len(feature_importances) + 1))}\n",
     "]\n",
     "\n",
@@ -2068,7 +2120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2106,7 +2158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.5"
   },
   "nav_menu": {
    "height": "279px",