M1
Text Processing
Tokenizer + vocabulary
A working tokenizer that turns raw text into clean token streams
Available
Free
- Effort
- 4-6 hours
- Prerequisite
- M0
- Core concept
- Normalization choices have consequences
What you have
Raw text strings
What you gain
Token streams and term frequency tables
What you build
This module turns raw product strings into consistent tokens and term frequency tables that later modules can index.
- A tokenize() function that normalizes raw text into repeatable token streams
- A build_vocabulary() function that records unique terms and frequencies
- Supporting Python files for normalization rules and token statistics
What you learn
- How casing, punctuation, stop words, and normalization choices affect recall
- Why token boundaries are design decisions, not fixed rules
- How vocabulary growth and term frequency shape the next indexing step
- How to test tokenization behavior against edge cases
Artifact and workload
Primary artifact: tokenize() and build_vocabulary() functions
Tests44
Assessments7
Estimated time4-6 hours
Access
This module is free. Read the overview here, then work through the code in the GitHub repository.