This multi-part workshop series provides enterprise Python developers and technical stakeholders with best practices for Understanding Natural Language User Requests end-to-end. It begins with a Foundational Introduction class (for both technical and non-technical audiences) to establish core concepts and context for natural language understanding. Subsequent workshops dive into technical implementation details, each targeting a specific stage of the NLU pipeline. The series is carefully structured to avoid repetition by clearly delineating which module covers each concept in depth. Attendees will gain a comprehensive, layered understanding – from basic text parsing to intent classification, entity extraction, semantic interpretation, ambiguity resolution, and continual improvement of NLU systems. 

Note: The introduction session covers high-level concepts (e.g. what intents and entities are, an overview of machine learning vs. rule-based approaches, etc.) to ensure all participants share a common vocabulary. Technical details and hands-on practice are reserved for the dedicated deep dives in Workshops 1–6. 

Input Parsing & Tokenization

This course focuses on fundamental text preprocessing. It covers how to break down raw user input into structured data (tokens, lexical categories) as the first step in understanding. This workshop is not about classifying meaning or building ML models – it is about linguistic preprocessing only. The primary audience is technical (Python developers, NLP engineers), though the concepts are accessible to cross-functional team members interested in the basics of NLP input processing.

Course Duration: 3 hours
Skill Level: Beginner