NLP Basics 3 Understanding Text Preprocessing Part 1

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=NTPVmpSQsPo

Understanding Text Pre-processing - • Corpus, Tokens, N-grams • Tokenization • Stemming • Lemmatization • According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured in nature. • Few notorious examples include – tweets / posts on social media, user to user chat conversations, news, blogs and articles, product or services reviews and patient records in the healthcare sector. A few more recent ones includes chatbots and other voice-driven bots. • Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system. • In order to produce significant and actionable insights from text data, it is important to get acquainted with the techniques and principles of Natural Language Processing (NLP). • To learn more here's a link to some of the resources - • Ultimate Guide to Understand and Implement Natural Language Processing (with codes in Python): https://www.analyticsvidhya.com/blog/... • The Essential NLP Guide for data scientists (with codes for top 10 common NLP tasks): https://www.analyticsvidhya.com/blog/... • A Comprehensive Learning Path to Understand and Master NLP in 2020: https://www.analyticsvidhya.com/blog/... • Introduction to Natural Language Processing: https://courses.analyticsvidhya.com/c... • To get more tutorials on NLP and machine learning, follow us on: • Linkedin - / anal. . • Facebook - / analyticsvid. . • Twitter - / analyticsvidhya • Instagram - / analytics_v. .

#############################

New on site