diff --git a/README.md b/README.md
index 767c76d..2160c41 100644
--- a/README.md
+++ b/README.md
@@ -30,7 +30,7 @@ We start with a high level overview of some foundational concepts in numerical l
 
 ### [2. Topic Modeling with NMF and SVD](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb) ([Video 2](https://www.youtube.com/watch?v=kgd40iDT8yY&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY&index=2) and [Video 3](https://www.youtube.com/watch?v=C8KEtrWjjyo&index=3&list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY))
 We will use the newsgroups dataset to try to identify the topics of different posts.  We use a term-document matrix that represents the frequency of the vocabulary in the documents.  We factor it using NMF, and then with SVD.
-  - [Topic Frequency-Inverse Document Frequency (TF-IDF)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#TF-IDF)
+  - [Term Frequency-Inverse Document Frequency (TF-IDF)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#TF-IDF)
   - [Singular Value Decomposition (SVD)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#Singular-Value-Decomposition-(SVD))
   - [Non-negative Matrix Factorization (NMF)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#Non-negative-Matrix-Factorization-(NMF))
   - [Stochastic Gradient Descent (SGD)](http://nbviewer.jupyter.org/github/fastai/numerical-linear-algebra/blob/master/nbs/2.%20Topic%20Modeling%20with%20NMF%20and%20SVD.ipynb#Gradient-Descent)