Natural Language Understanding with Python

Table of Contents

1. Natural Language Understanding, Related Technologies, and Natural Language Applications
2. Identifying Practical Natural Language Understanding Problems
3. Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning
4. Selecting Libraries and Tools for Natural Language Understanding
5. Natural Language Data – Finding and Preparing Data
6. Exploring and Visualizing Data
7. Selecting Approaches and Representing Data
8. Rule-Based Techniques
9. Machine Learning Part 1 - Statistical Machine Learning
10. Machine Learning Part 2 – Neural Networks and Deep Learning Techniques
11. Machine Learning Part 3 – Transformers and Large Language Models
12. Applying Unsupervised Learning Approaches
13. How Well Does It Work? – Evaluation
14. What to Do If the System Isn't Working
15. Summary and Looking to the Future