diff --git a/notebooks/00.00-Preface.ipynb b/notebooks/00.00-Preface.ipynb
index 6b1e69233..8eca9ec48 100644
--- a/notebooks/00.00-Preface.ipynb
+++ b/notebooks/00.00-Preface.ipynb
@@ -111,7 +111,7 @@
     "\n",
     "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n",
     "\n",
-    "1. IPython and Jupyter: these packages provide the the computational environment in which many Python-using data scientists work.\n",
+    "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n",
     "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n",
     "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n",
     "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n",
diff --git a/notebooks/05.11-K-Means.ipynb b/notebooks/05.11-K-Means.ipynb
index 7e859a14a..e0c8a6ed0 100644
--- a/notebooks/05.11-K-Means.ipynb
+++ b/notebooks/05.11-K-Means.ipynb
@@ -412,7 +412,7 @@
     "We might imagine using the same trick to allow *k*-means to discover non-linear boundaries.\n",
     "\n",
     "One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator.\n",
-    "It uses the the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
+    "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
    ]
   },
   {