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Introduction

Curious about how ChatGPT and other Large Language Models actually work? This course brings you one step closer to this type of understadning by giving you an introduction to Natural Language Processing applied in a business setting. 

Business decisions are becoming more and more dependent on quantitative analysis and data. Data can come in many formats. In today’s data rich world traditional data, where the information is represented as numbers in ordered tables, is by far outweighed by the large amount of information embedded in textual data sources. Examples include this course description, newspaper articles, social media postings, customer reviews, books, and financial reports. 

However, to be able to extract business relevant information from textual data we need to know how to turn something potentially very high dimensional and unstructured into something useful for quantitative analysis. This is called Natural Language Processing (NLP).

This introductory course in NLP is designed to provide business students with a comprehensive foundation in NLP, enabling them to leverage language-based technologies to enhance communication, customer engagement, decision-making, and innovation within various industries and fields. Themes covered include sentiment analysis, topic modelling, classification and trend analysis. 

Through a blend of theoretical concepts and hands-on practical exercises, students will explore the fundamental principles of NLP, its applications, and its impact on modern business practices.  Prior exposure to quantitative subjects within programming, statistics and machine learning is an advantage, but not a requirement.

Course content

  • Introduction to Natural Language Processing
    • Historical context and key milestones in NLP developments
    • Text processing, tokenization, and language modelling
    • Business case examples
    • Data sources
  • Text preprocessing
    • Cleaning and preparing textual data for analysis
    • Techniques for text normalization, stemming, and lemmatization
  • Algorithms
    • Supervised methods
      • Boolean/Dictionary-based
      • Regression/classification-based
    • Unsupervised methods
      • Topic models
      • Embeddings
  • AI and Large Language Models (LLM)
    • Building basic NLP pipelines for various business scenarios
    • Fine-tuning LLMs for specific tasks
  • Case studies and real-world applications

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.