Framework First
Stage 02 · Automateintermediate Coding exercisesPython, AI & Machine Learning

NLP with Python — Text Intelligence in Practice

Learn how machines read text, then use Python to build real text intelligence applications. From tokenization to transformers — production NLP features, shipped.

4 hours 7 modules

The framework

Collect-Clean-Represent-Model-Interpret (CCRMI)

CCRMI guides every NLP project: (C) Collect text data with purpose; (C) Clean and normalise for your specific task; (R) Represent text numerically using the right method for your problem; (M) Model with the simplest approach that meets your accuracy threshold; (I) Interpret outputs in business terms.

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How you'll learn

Text-first. Practice-heavy. No video lectures.

15-minute text lessons

Short, dense reading you can finish between meetings — not hour-long video lectures.

Hands-on exercises

Apply the framework to a realistic scenario, right in the module where you learned it.

Quizzes & assessments

Check your judgment, not just your memory — most questions are situational.

Certificate on completion

Finish a course and get a shareable certificate, automatically.

About this course

Natural Language Processing is one of the most commercially valuable AI skills. This course teaches the CCRMI framework — from classical TF-IDF to transformer embeddings to LLM-powered RAG pipelines. Updated for 2026: the course covers when to use a classical pipeline, when to prompt an LLM, and when to build a RAG system. You will learn to choose the right tool for the right task.

The project: Customer Review Intelligence Pipeline

Build a pipeline that processes 1,000 real Amazon reviews, extracts sentiment, identifies key topics, and flags named entities — then presents findings in a structured report.

  1. 1

    Why NLP Fails in Production

    Real failure cases and the CCRMI framework introduction.

    30 min· Knowledge check
    Free preview
  2. 2

    Text Collection & Cleaning

    Scraping, noise removal, and normalisation for NLP.

    30 min· Knowledge check· Coding exercise
  3. 3

    Representing Text as Numbers — From Bag-of-Words to Embeddings

    TF-IDF, word embeddings, and when to use each.

    30 min· Knowledge check· Coding exercise
  4. 4

    Classification & Sentiment Models

    Logistic regression vs transformers decision framework.

    30 min· Knowledge check· Coding exercise
  5. 5

    Named Entity Recognition in Practice

    spaCy, custom NER, and business use cases.

    30 min· Knowledge check· Coding exercise
  6. 6

    Modern NLP with LLMs, RAG & Vector Databases ★ NEW

    50 min· Knowledge check
  7. 7

    Capstone — Customer Review Intelligence Pipeline

    Build a complete NLP pipeline on real Amazon reviews.

    30 min· Knowledge check· Coding exercise