fixed links for knn

Table_of_Contents.ipynb

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@@ -75,7 +75,7 @@
      "source": [
       "1.  [Spectral Features](notebooks/spectral_features.ipynb)\n",
       "1.  [Mel-Frequency Cepstral Coefficients](notebooks/mfcc.ipynb)\n",
-      "1.  [K-Nearest Neighbor Instrument Classification](exercises/knn_instrument_classification.ipynb)"
+      "1.  [K-Nearest Neighbor Instrument Classification](notebooks/knn_instrument_classification.ipynb)"
      ]
     },
     {

notebooks/knn_instrument_classification.ipynb

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@@ -13,16 +13,16 @@
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      "source": [
-      "k-Nearest Neighbour"
+      "K-Nearest Neighbor"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "My first audio classifier: introducing K-NN!  \n",
+      "My first audio classifier: introducing K-NN!\n",
       "\n",
-      "We can now appreciate why we need additional intelligence in our systems - heuristics can't go very far in the world of complex audio signals.  We'll be using scikit.learn's implementation of the k-NN for our work here.  It proves be a straight-forward and easy to use implementation.  The steps and skills of working with one classifier will scale nicely to working with other, more complex classifiers."
+      "We can now appreciate why we need additional intelligence in our systems -- heuristics don't go very far in the world of complex audio signals.  We'll be using scikit-learn's implementation of the k-NN for our work here.  It proves be a straight-forward and easy to use implementation.  The steps and skills of working with one classifier will scale nicely to working with other, more complex classifiers."
      ]
     },
     {