Update README.md

Author: dipanjanS

README.md

@@ -12,20 +12,20 @@ Keep watching this space!
 
 ## Get the book 
 <div>
-<a target="_blank" href="http://www.apress.com/us/book/9781484223871">
-  <img src="./media/banners/apress_logo.png" alt="apress" align="left"/>
+<a target="_blank" href="https://www.apress.com/gp/book/9781484243534">
+  <img src="https://i.imgur.com/FI3L387.png" alt="apress" align="left"/>
 </a>
-<a target="_blank" href="http://link.springer.com/book/10.1007%2F978-1-4842-2388-8">
-  <img src="./media/banners/springer_logo.png" alt="springer" align="left"/>
+<a target="_blank" href="https://link.springer.com/book/10.1007/978-1-4842-4354-1">
+  <img src="https://i.imgur.com/twty9FD.png" alt="springer" align="left"/>
 </a>
-<a target="_blank" href="https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X/ref=sr_1_1?ie=UTF8&qid=1481143141&sr=8-1&keywords=text+analytics+with+python">
-  <img src="./media/banners/amazon_logo.jpg" alt="amazon" align="left"/>
+<a target="_blank" href="https://www.amazon.com/Text-Analytics-Python-Practitioners-Processing/dp/1484243536/ref=sr_1_1?dchild=1&keywords=Text+Analytics+with+Python&qid=1599726217&sr=8-1">
+  <img src="https://i.imgur.com/CRWnPje.png" alt="amazon" align="left"/>
 </a>
-<a target="_blank" href="https://books.google.co.in/books?id=IimgDQAAQBAJ&dq=text+analytics+with+python&source=gbs_navlinks_s">
-  <img src="./media/banners/googlebooks_logo.png" alt="google" align="left"/>
+<a target="_blank" href="https://www.google.com/books/edition/Text_Analytics_with_Python/arWZDwAAQBAJ?hl=en&gbpv=0">
+  <img src="https://i.imgur.com/x3AEDsf.png" alt="google" align="left"/>
 </a>
-<a target="_blank" href="https://play.google.com/store/books/details/Dipanjan_Sarkar_Text_Analytics_with_Python?id=IimgDQAAQBAJ">
-  <img src="./media/banners/googleplay_logo.png" alt="google" align="left"/>
+<a target="_blank" href="https://play.google.com/store/books/details/Dipanjan_Sarkar_Text_Analytics_with_Python?id=arWZDwAAQBAJ">
+  <img src="https://i.imgur.com/vKEIiRp.png" alt="google" align="left"/>
 </a>
 <br>
 </div>
@@ -43,28 +43,28 @@ You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled w
 Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.
 
 <div style='font-size:0.5em;'>
-<sup>Edition: 1st<br>
-Pages: 385<br>
+<sup>Edition: 2nd<br>
+Pages: 674<br>
 Language: English<br>
 Book Title: Text Analytics with Python<br>
+Book Subtitle: A Practitioner's Guide to Natural Language Processing<br>
 Publisher: Apress (a part of Springer)<br>  
-Print ISBN: 978-1-4842-2387-1<br>  
-Online ISBN: 978-1-4842-2388-8<br>
-DOI: 10.1007/978-1-4842-2388-8<br>
+Print ISBN: 978-1-4842-4353-4<br>  
+Online ISBN: 978-1-4842-4354-1<br>
+DOI: 10.1007/978-1-4842-4354-1<br>
 Copyright: Dipanjan Sarkar<br></div>
 
 <br>
 
-This book:
- - Provides complete coverage of the major concepts and 
- techniques of natural language processing (NLP) and text analytics
- - Includes practical real-world examples of techniques for implementation, 
-  such as building a text classification system to categorize news articles, 
-  analyzing app or game reviews using topic modeling and text summarization, 
-  and clustering popular movie synopses and analyzing the sentiment of movie reviews
- - Shows implementations based on Python and several popular open source libraries 
+With this book you will:
+- Understanding NLP and text syntax, semantics and structure
+- Discover text cleaning and feature engineering strategies
+- Learn and implement text classification and text clustering
+- Understand and build text summarization and topic models
+- Learn about the promise of deep learning and transfer learning for NLP
+- Implement hands-on examples based on Python and several popular open source libraries 
  in NLP and text analytics, such as the natural language toolkit (`nltk`), 
- `gensim`, `scikit-learn`, `spaCy` and `Pattern`
+ `gensim`, `scikit-learn`, `spaCy`, `keras` and `tensorflow`