docs: Edit formatting of unordered list in the credits

GitHub markdown requires double-space indents for unordered lists (as opposed to Jupyter Notebooks)

README.md

@@ -8,11 +8,11 @@ A deep learning (LSTM) sentiment analysis project to determine positive/negative
 
 ## credits:
 
- - Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec
-  - https://github.com/iamaziz/ar-embeddings
-  - A. Altowayan and L. Tao "Word Embeddings for Arabic Sentiment Analysis", IEEE BigData 2016 Workshop
- - Twitter Data set for Arabic Sentiment Analysis Data Set
-  - https://archive.ics.uci.edu/ml/datasets/Twitter+Data+set+for+Arabic+Sentiment+Analysis
-  - Abdulla N. A., Mahyoub N. A., Shehab M., Al-Ayyoub M.,Arabic Sentiment Analysis: Corpus-based and Lexicon-based,IEEE conference on Applied Electrical Engineering and Computing Technologies (AEECT 2013),December 3-12, 2013, Amman, Jordan. (Accepted for Publication).
- - arabic-stop-words; 750 words in a text file
-  - https://github.com/mohataher/arabic-stop-words
+  - Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec
+    - https://github.com/iamaziz/ar-embeddings
+    - A. Altowayan and L. Tao "Word Embeddings for Arabic Sentiment Analysis", IEEE BigData 2016 Workshop
+  - Twitter Data set for Arabic Sentiment Analysis Data Set
+    - https://archive.ics.uci.edu/ml/datasets/Twitter+Data+set+for+Arabic+Sentiment+Analysis
+    - Abdulla N. A., Mahyoub N. A., Shehab M., Al-Ayyoub M.,Arabic Sentiment Analysis: Corpus-based and Lexicon-based,IEEE conference on Applied Electrical Engineering and Computing Technologies (AEECT 2013),December 3-12, 2013, Amman, Jordan. (Accepted for Publication).
+  - arabic-stop-words; 750 words in a text file
+    - https://github.com/mohataher/arabic-stop-words