Update README.md

README.md

@@ -30,19 +30,19 @@ space the goal of the algorithm is to find dense clusters of documents, then ide
 words attracted those documents together. Each dense area is a topic and the words that
 attracted the documents to the dense area are the topic words.
 
-**The Algorithm:**
+# The Algorithm:
 
-1. Create jointly embedded document and word vectors using [Doc2Vec](https://radimrehurek.com/gensim/models/doc2vec.html).
+**1. Create jointly embedded document and word vectors using [Doc2Vec](https://radimrehurek.com/gensim/models/doc2vec.html).**
 >Documents will be placed to other similar documents and close to most distinguishing words. 
 ![Joint Document and Word Embedding](images/doc_word_embedding.svg)
-2. Create lower dimensional embedding of document vectors using [UMAP](https://github.com/lmcinnes/umap)
+**2. Create lower dimensional embedding of document vectors using [UMAP](https://github.com/lmcinnes/umap)**
 >Document vectors in high dimensional space are very sparse, dimension reduction allows the discovery of dense areas. 
 ![UMAP dimension reduced Documents](images/umap_docs.png)
-3. Find dense areas of documents using [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan)
+**3. Find dense areas of documents using [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan)**
 >The colored areas are the dense areas of documents. Red points are outliers that do not belong to a specific topic.
 ![HDBSCAN Document Clusters](images/hdbscan_docs.png)
-4. For each dense area calculate centroid of document vectors in original dimension. (centroid = topic vector)
-5. Find n-closest word vectors to the resulting topic vector
+**4. For each dense area calculate centroid of document vectors in original dimension. (centroid = topic vector)**
+**5. Find n-closest word vectors to the resulting topic vector**
 
 
 Installation