diff --git a/README.md b/README.md
index b57adce..f6f0f17 100644
--- a/README.md
+++ b/README.md
@@ -94,14 +94,14 @@ Credits: Forked from [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakev
| Notebook | Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [scikit-learn-intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) | Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
-| [scikit-learn-knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | K-Nearest Neighbors. |
-| [scikit-learn-linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Linear regression. |
-| [scikit-learn-svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Support vector machine classifier, with and without kernels. |
-| [scikit-learn-random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Random forest classifier and regressor. |
-| [scikit-learn-k-means](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) | K-Means Clustering. |
-| [scikit-learn-pca](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) | Principal Component Analysis. |
-| [scikit-learn-validation](#scikit-learn) | Coming Soon. |
+| [intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) | Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
+| [knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | K-Nearest Neighbors. |
+| [linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Linear regression. |
+| [svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Support vector machine classifier, with and without kernels. |
+| [random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Random forest classifier and regressor. |
+| [k-means](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) | K-Means Clustering. |
+| [pca](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) | Principal Component Analysis. |
+| [validation](#scikit-learn) | Coming Soon. |