Basics of NLP and Natural Language Models (LLM1)

This course provides practical exposure to NLP techniques in the insurance industry. Students will learn to apply NLP, LLMs, and Gen AI through practical use cases and techniques, and explore strategic implementation.

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Course curriculum

1. Welcome Screen
2. How to use the Actuartech platform
3. Software requirements
  FREE PREVIEW
1. 0.1 - Course Overview
  FREE PREVIEW
2. 0.2 - Introducing Python
3. 0.3 - How to get set up
4. 0.4 - A User Guide for Jupyter Notebooks
5. 0.5 - Other ways to code in Python
1. Slide deck: Chapter 1 (Text Mining)
2. Notebook: Chapter 1 (Text Mining)
3. Notebook appendix: Chapter 1 (Text Mining - Deep Learning Example)
4. Key references: Chapter 1 (Text Mining)
5. Packages required: Chapter 1 (Text Mining)
6. Downloadable notebooks and data: Chapter 1 (Text Mining)
1. Slide deck: Chapter 2 (Embedding)
2. Notebook: Chapter 2 (Embedding)
3. Notebook appendix: Chapter 2 (Embeddings)
4. Key references: Chapter 2 (Embedding)
5. Packages required: Chapter 2 (Embedding)
6. Downloadable notebooks and data: Chapter 2 (Embedding)
1. Slide deck: Chapter 3 (NLP Tasks)
2. Notebook: Chapter 3 (Part 1) (NLP Tasks)
3. Notebook: Chapter 3 (Part 2) (NLP Tasks)
4. Key references: Chapter 3 (NLP Tasks)
5. Packages required: Chapter 3 (NLP Tasks)
6. Downloadable notebooks and data: Chapter 3 (NLP Tasks)
1. Slide deck: Chapter 4 (GenAI)
2. Notebook: Chapter 4 (GenAI)
3. Key references: Chapter 4 (GenAI)
4. Packages required: Chapter 4 (GenAI)
5. Downloadable notebooks and data: Chapter 4 (GenAI)
1. Mini Project Instructions
2. Mini Project Data
3. Mini Project Submission
1. A1. Natural Language Processing in Insurance - A. Mashechkin (for the IFoA)
2. A2.1. Reinsurance treaties study using natural language processing: Methods and innovation enablers for actuaries - A. Couloumy & L. Chiapparo
3. A2.2. Presentation: Reinsurance treaties study using natural language processing: Methods and innovation enablers for actuaries - A. Couloumy
4. A3. Deep learning insights to handle NLP(re)insurance domain issues: A claims analysis perspective - A. Couloumy
5. A4.1. Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial Context - A. Troxler & J. Schelldorfer
6. A4.2. Presentation: Actuarial Applications of Natural Language Processing - J. Schelldorfer
7. A4.3. Notebook: Actuarial Applications of Natural Language Processing Using Transformers Case Studies for Using Text Features in an Actuarial Context - A. Troxler & J. Schelldorfer
8. A5. Applications of Modern NLP Techniques for Predictive Modeling in Actuarial Science - S. Xu
9. A6. Framework of BERT-Based NLP Models for Frequency and Severity in Insurance Claims - S. Xu, V. Manathunga & D. Hong
10. A7. ActuaryGPT: Applications of large language models to insurance and actuarial work - C. Balona
11. A8. The Credibility Transformer - R. Richman
12. A9. Reinforcement Fine-Tuning - OpenAI
13. A10. Operationalizing LLMs - A Guide for Actuaries - C. Balona

About this course

£325.00
47 lessons
0 hours of video content

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Basics of NLP and Natural Language Models (LLM1)

Course curriculum

Welcome

0. Getting Started

1. Text Mining

2. Embedding

3. NLP Tasks

4. Generative AI

Mini Project

Additional Resources

About this course

Discover your potential, starting today