Complete for freeAdvancedBrowser simulator included

Course 21

Natural Language Processing and Generative AI

Explore tokenization, embeddings, classification, entities, transformers, attention, prompts, structured output, retrieval, hallucination checks, adaptation, evaluation, and responsible deployment. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines NLP and retrieval concept simulations with learner-created prompt, document, and evaluation files. The C4T page does not supply documents, embeddings, or model output and does not call a paid API.

Before you begin

Requirements & setup for Natural Language Processing and Generative AI

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

The always-free pathway combines NLP and retrieval concept simulations with learner-created prompt, document, and evaluation files. The C4T page does not supply documents, embeddings, or model output and does not call a paid API.

$0 required

Setup checklist

0 of 6 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

  1. Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.

    About 2 min

  2. Open the concept simulator and read its scenario, assumptions, and limitations.

    About 5 minOpen C4T Local NLP and Retrieval Concept Simulator (opens in a new tab)

  3. Make a prediction, adjust the available controls, and compare the result with the explanation.

    About 5 min

  4. Record what changed and why; repeat with at least one different input.

    About 5 min

  5. For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

    About 5 min

  6. C4T Local NLP and Retrieval Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file.

    About 5 min

Software

1 item

C4T Local NLP and Retrieval Concept Simulator

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Course need
C4T Local NLP and Retrieval Concept Simulator
Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

  1. Open the concept simulator and read its scenario, assumptions, and limitations.
  2. Make a prediction, adjust the available controls, and compare the result with the explanation.
  3. Record what changed and why; repeat with at least one different input.
  4. For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
Open C4T Local NLP and Retrieval Concept Simulator (opens in a new tab)

No-hardware path

Local NLP and Retrieval Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Optional paid alternatives

You do not need these options to complete the course.

Commercial language-model API

Optional comparison only; never required and never used with private course documents.

Optional only—never required for course completion.

Hardware

Nothing required

No physical hardware is needed for this course.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

  • Inspect tokenization, embeddings, similarity, classification, and entity labels.
  • Explain transformers, attention, tokens, and context limits.
  • Validate prompts and structured outputs against explicit schemas.
  • Build and evaluate a privacy-aware grounded document question-answering system.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Text representation and labeling lab

Build a small tokenization, similarity, classification, and entity-label evaluation with documented errors.

The always-free pathway combines NLP and retrieval concept simulations with learner-created prompt, document, and evaluation files. The C4T page does not supply documents, embeddings, or model output and does not call a paid API. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Grounded answer evaluator

Use learner-selected non-sensitive documents to plan chunks, rank passages, validate an answer structure, and score citation support and hallucinations.

The always-free pathway combines NLP and retrieval concept simulations with learner-created prompt, document, and evaluation files. The C4T page does not supply documents, embeddings, or model output and does not call a paid API. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Final assessment

Grounded document question-answering system

Build a local document workflow with ingestion, chunking, retrieval, grounded answers, citations, structured output, hallucination checks, evaluation data, privacy safeguards, and documented limits. Deliverables: Safe document ingestion and chunking, Inspectable retrieval and ranking, Grounded structured answers with citations, Hallucination and evaluation dataset, and Privacy, limitations, and fallback behavior.

Prerequisites & pacing

Skills assumed

  • Python ML workflows and model-evaluation fundamentals

Recommended prior courses

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

  • self paced: 12 weeks at 5 hours per week
  • weekly: 12 weeks at 5 hours per week

Part of this learning path

Course created by Zafir Shamsi.