From 11ec8368dea759bbb074ede158a762586996d610 Mon Sep 17 00:00:00 2001
From: Jaques Grobler <jaquesgrobler@gmail.com>
Date: Wed, 15 Jan 2014 12:23:05 +0100
Subject: [PATCH] small fixes

---
 .../text_analytics/working_with_text_data.rst      | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/doc/tutorial/text_analytics/working_with_text_data.rst b/doc/tutorial/text_analytics/working_with_text_data.rst
index d4b42bf2e8a3e..29eceb85ec378 100644
--- a/doc/tutorial/text_analytics/working_with_text_data.rst
+++ b/doc/tutorial/text_analytics/working_with_text_data.rst
@@ -537,31 +537,31 @@ Bonus point if the utility is able to give a confidence level for its
 predictions.
 
 
-Where to go from Here
+Where to from here
 ------------------
 
 Here are a few suggestions to help further your scikit-learn intuition
 upon the completion of this tutorial:
 
 
-- Try playing around with the `analyzer` and `token normalisation` under
+* Try playing around with the ``analyzer`` and ``token normalisation`` under
   :class:`CountVectorizer`
 
-- If you don't have labels, try using
+* If you don't have labels, try using
   :ref:`Clustering <example_document_clustering.py>`
   on your problem.
 
-- If you have multiple labels per document, e.g categories, have a look
+* If you have multiple labels per document, e.g categories, have a look
   at the :ref:`Multiclass and multilabel section <multiclass>`
 
-- Try using :ref:`Truncated SVD <LSA>` for
+* Try using :ref:`Truncated SVD <LSA>` for
   `latent semantic analysis <http://en.wikipedia.org/wiki/Latent_semantic_analysis>`_.
 
-- Have a look at using
+* Have a look at using
   :ref:`Out-of-core Classification
   <example_applications_plot_out_of_core_classification.py>` to
   learn from data that would not fit into the computer main memory.
 
-- Have a look at the :ref:`Hashing Vectorizer <hashing_vectorizer>`
+* Have a look at the :ref:`Hashing Vectorizer <hashing_vectorizer>`
   as a memory efficient alternative to :class:`CountVectorizer`.