What is Natural Language Processing (NLP)?

Defining NLP • Defining natural languages, and contrasting them from constructed and formal languages • Examining the ways that computers handle natural language as numbers (data) • Recognizing simple applications for computer processing of natural language data NLP History • Identifying early thinkers who speculated about computation, computers, and human language • Analyzing successes and failures in early NLP experiments in machine translation and chatbots • Examining key enabling technologies that supported the advancement of NLP over the past few decades NLP Applications • Relating early NLP experiments to modern applications for individuals and organizations • Describing specific sources of value brought by NLP applications to businesses, such as in customer engagement and sentiment analysis • Considering the future of common NLP applications Working With Text • Identifying computer processes (algorithms) useful for handling and transforming text • Recognizing common challenges in working with textual data, such as issues related to data quality • Analyzing components of NLP frameworks, and linking them to common NLP methods such as named-entity recognition (NER) From Models to Systems • Differentiating text-level NLP methods from NLP systems • Relating system-level NLP to NLP applications • Recognizing the intuitions behind two complex NLP systems: Information Extraction and Conversational AI

The course is intended primarily for learners with a focus on business and/or technology, and specifically those seeking to better understand what NLP is, where it came from, and how it can benefit their organizations. Although the course does not cover the technical aspects of building NLP systems, Data Scientists, Machine Learning Engineers, and other technically-inclined learners may still find concepts and best practices introduced in the subject matter to be useful.

No prerequisite knowledge is assumed, however a basic understanding of artificial intelligence and machine learning (i.e. what they are, what they do) will be beneficial.

• Defining NLP • NLP History • NLP Applications • Working With Text • From Models to Systems