diff --git a/08_dimensionality_reduction.ipynb b/08_dimensionality_reduction.ipynb
index fc665c9..e5ef84c 100644
--- a/08_dimensionality_reduction.ipynb
+++ b/08_dimensionality_reduction.ipynb
@@ -2606,7 +2606,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to $(a - b)/\\max(a, b)$ where $a$ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and $b$ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes $b$, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster."
+    "Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to $(b - a)/\\max(a, b)$ where $a$ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and $b$ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes $b$, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     "        coeffs = silhouette_coefficients[y_pred == i]\n",
     "        coeffs.sort()\n",
     "\n",
-    "        color = matplotlib.cm.spectral(i / k)\n",
+    "        color = matplotlib.cm.Spectral(i / k)\n",
     "        plt.fill_betweenx(np.arange(pos, pos + len(coeffs)), 0, coeffs,\n",
     "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
     "        ticks.append(pos + len(coeffs) // 2)\n",