Introduction to Natural Language Processing

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Course Description

Course Overview:

This course is designed for beginners through intermediate learners with basic Python understanding and aims to provide a solid foundational introduction to Natural Language Processing (NLP), including an exploration of Generative AI Large Language Models (LLMs). The course will progress starting with the history and basics of NLP and concepts will be presented in clear terms, demystifying NLP jargon. The course will be structured in a modular, asynchronous format, allowing learners to move forward or backward at their own pace and providing the ability to jump to specific topics of interest. The course will provide progressively more complex theoretical, conceptual and hands-on applications in Python in every module as learners advance.

This course will cover the following topics:

Introduction to NLP
History of NLP
Computation of linguistic knowledge
Lexical semantics, Syntactic analysis, Semantic analysis and Pragmatics
Machine learning for NLP
The Transformer Model
LLMs for NLP
LLM tools and APIs
LLM Models and training
Applications of LLMs in the medical domain
Research in NLP and LLMs
Future Directions of Generative AI

Course Objectives:

Upon completion of this course, students will be able to:

Understand the basic concepts of NLP
Describe the history of NLP
Compute linguistic knowledge
Use lexical semantics
Perform syntactic analysis
Perform semantic analysis
Understand pragmatics
Explain Transformers
Use machine learning for NLP
Use LLMs for NLP
Apply LLMs in the medical domain

Course Structure:

Module 0: Introduction to NLP and its Applications
Module 1: Text Classification and Sentiment Analysis and Named Entity Recognition
Module 2: Introduction to Large Language Models (LLMs)
Module 3: LLM Terminology, Techniques and Tools
Module 4: Deployment and Scaling of NLP Models
Module 5: NLP for Medical Practice and Research
Module 6: Future Directions and Open Research Problems in NLP