Natural Language Processing

About the Course

Course Contents

  •  Introduction to NLP
  • What can we achieve by combining simple programming techniques with large quantities of text? How can we automatically extract key words and phrases that sum up the style and content of a text? What tools and techniques does the Python programming language provide for such work? What are some of the interesting challenges of natural language processing?
  •  Analyzing a Bag of Words
  • A bag of words can be analyzed using several in-built tools within the NLTK packages. Here you will learn about dispersion plots, grammar, slicing and dicing of words and edit distances. This chapter requires hands-on Python skills.
  •  Machine Learning
  • Naive Bayes, Logistic Regression and Classification techniques can be applied to Natural Language Processing. This field has evolved enormously and is now growing into Deep Learning and Machine Translation. This chapter shows the implementation of such tools in NLP
  •  Case Study 1
  • Newspaper headlines have been collected from various sources. The objective of this case study is to analyze the news and identify the common themes during a particular period of time.
  •  Case Study 2
  • Restautants collect feedback from customers. This feedback is both quantitative and qualitative. The objective of this case is to perform a sentiment analysis and determine if the quantitative and qualitative assessment makes sense.

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