NLP: Data foundations
Apply NLP through problem framing, data types, tool setup.
Advanced / Structured learning draft / Version 0.9
Structured learning draftNatural Language Processing
Turn untidy data into a defensible finding. You will finish with a cleaned dataset, analysis and decision-ready chart.
This open learning manuscript is undergoing course-specific editorial review. It does not provide certification. Progress and notes are stored only in this browser.
Data & PythonTool stack
PyTorchPython
12guided lessons
4focused modules
1reviewable project
PDFoffline workbook
The problem
What this course helps you do
Natural Language Processing is a structured, practical course covering NLP, Transformers, BERT, LLMs. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Who it is for
Learners using data and code to answer practical questions.
Prerequisites
- Basic numeracy and file-management skills
Portfolio-ready project
Produce a reviewable Natural Language Processing project using NLP, Transformers, BERT.
Deliverable: A working Natural Language Processing artefact plus an evidence-based self-review.
Practice fileAnalysis practice datasetFictional monthly service data with units and dates.Planning template · MarkdownProject briefDefine the user, outcome, boundaries, evidence and acceptance criteria before building.Printable workbook · PDFComplete course workbookA4-ready lesson prompts, project worksheets, evidence logs and references.
Downloads open without an account. Practice data is fictional; review each file before using it in another system.
Definition of done
- The output matches the stated outcome.
- Inputs and decisions are reproducible.
- Boundary and failure evidence is included.
- Limitations and responsibility considerations are explicit.
Course plan
From first concept to finished work
Transformers: Analysis
Apply Transformers through cleaning, exploration, modelling.
BERT: Evaluation
Apply BERT through validation, uncertainty, communication.
LLMs: Applied project
Apply LLMs through workflow, review, reproducibility.
Source-led learning
Authoritative references
Course claims are grounded in primary documentation and recognized public guidance. Recheck version-sensitive information before professional use.
- The Python TutorialPython Software Foundation
- User Guidescikit-learn