About Dipanjan Sarkar

Dipanjan Sarkar is a Data Science Lead at Applied Materials, a Google Developer Expert in Machine Learning, a published author, and an editor at Towards Data Science. Feel free to connect with him on LinkedIn.

Dipanjan Sarkar Posts (13)

• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - 06 Dec 2018
  
  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
• Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP - 24 Aug 2018
  
  Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!
• Named Entity Recognition: A Practitioner’s Guide to NLP - 17 Aug 2018
  
  Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
• Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP - 10 Aug 2018
  
  Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.
• Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP - 03 Aug 2018
  
  I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.
• Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP - 26 Jul 2018
  
  Proven and tested hands-on strategies to tackle NLP tasks.
• Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation - 06 Jun 2018
  
  A brief introduction into machine learning model interpretation.
• Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - 01 May 2018
  
  Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.
• Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - 25 Apr 2018
  
  The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
• Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - 17 Apr 2018
  
  The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.