Recommended prerequisite: C|AIMLP™ & proficiency in Python 3

Modules

Days

80+

Hours

Hands-on Labs

150

Exam Questions

∞

Cert Validity

Day 1 — Foundations of NLP & Transformers

Module 1

Introduction to Generative AI & NLP Techniques

Set up your advanced AI environment and understand core NLP concepts — tokens, embeddings, transformers, and attention mechanisms.

What is Generative AI? Tokens & Embeddings Transformers & Attention LLaMA 3, Mistral & API Setup Preprocessing & Normalisation Lab 0

You can set up advanced AI/NLP tools and understand transformer architectures

Module 2

Foundational NLP with Pre-trained Models

Work with tokenisation, embeddings, RNNs, Word2Vec, GloVe, and TF-IDF feature engineering on real text data.

Tokenisation & Embeddings RNNs & Sequential Data Word2Vec & GloVe Noisy/Missing Data TF-IDF Feature Engineering Labs 1, 2, 7, 8, 9

You can build foundational NLP pipelines with embeddings and feature engineering

Day 2 — Fine-Tuning & Prompt Engineering

Module 3

Fine-Tuning LLMs

Specialise pre-trained models using instruction-tuning, domain adaptation, LoRA, and parameter-efficient techniques.

Advanced Fine-Tuning Instruction-Tuning Domain Adaptation LoRA & Adapters Parameter-Efficient Methods Labs 3, 4, 5, 10A-C

You can fine-tune and train local LLMs for domain-specific tasks

Module 4

Applied Prompt Engineering

Design effective prompts — chain-of-thought reasoning, zero-shot, few-shot, and multi-shot strategies for production use.

Custom Task Prompts Chain-of-Thought Reasoning Zero-shot & Few-shot Multi-shot Prompts Evaluation & Deployment

You can engineer prompts that produce accurate, reliable LLM outputs

Day 3 — NER, Multilingual & Multimodal

Module 5

Named Entity Recognition (NER)

Extract entities from text — people, places, organisations — using NER models with real-world applications and ethical considerations.

Entity Recognition Models NER Applications Steps & Challenges Ethics in NER Lab 6 (Python & C#)

You can extract and classify named entities from text data

Module 6

Translation & Multilingual Summarisation

Build translation pipelines, tackle multilingual challenges, and summarise across languages using GPT-5.2, Gemini, and LLaMA 3.

Machine Translation Translation Challenges Multilingual Summarisation Translation Pipelines Real-World Applications Labs 11–14

You can build multilingual translation and summarisation systems

Module 7

Multimodal Content Generation

Work with text-to-image, image-to-text, and cross-modal models — Gemini, BLIP, Grok — for industry applications.

What is Multimodal Generation? Gemini, BLIP & Grok Industry Applications Multimodal Pipelines Ethical Challenges Lab 15

You can generate and process multimodal content across text and images

Day 4 — Deployment & Dashboarding

Module 8

Model Deployment (Web, Python & C#)

Deploy AI models via web, Python, and C# integrations — deployment workflows, case studies, and ethical deployment practices.

Deployment Workflow Web Integration Python & C# Deployment Case Studies Ethics in Deployment Labs 16–18

You can deploy LLMs to web, Python, and C# applications

Module 9

Building Multimodal Dashboards

Build interactive dashboards with Streamlit, Dash, and Blazor — connecting LLMs to live data and domain-specific use cases.

Streamlit, Dash & Blazor Dashboard Components Connecting LLMs Sector Use Cases Best Practices Labs 19–20

You can build AI-powered dashboards that connect LLMs to real data

Day 5 — Pipelines, Real-Time Systems & Capstone

M10

Module 10

Comprehensive Pipeline Implementation

Build end-to-end NLP and multimodal pipelines with real-time processing, deployment, and monitoring.

NLP Pipelines Multimodal Pipelines Real-Time Processing Deployment & Monitoring

You can architect complete AI pipelines from ingestion to production

M11

Module 11

Deploying Models to Applications

Advanced deployment — web app integration, Python & C# API integration, and real-world case studies at scale.

Advanced Deployment Workflow Web App Integration Python & C# API Integration Case Studies at Scale

You can integrate AI models into production applications across platforms

M12

Module 12

Building Multimodal Dashboards (Advanced)

Advanced dashboard development — sector-specific use cases for cybersecurity, finance, healthcare, retail, and government.

Advanced Dashboard Components Development Frameworks Model-to-Dashboard Connections Cybersecurity Dashboards Finance & Healthcare Labs 21–26

You can build sector-specific multimodal dashboards with live LLM integration

M13

Module 13

Full Pipeline Case Studies

Capstone integration — real-time threat detection in cybersecurity, financial forecasting pipelines, monitoring, scaling, and evaluation.

Real-Time Threat Detection Financial Forecasting Pipeline Monitoring & Scaling Evaluation & Governance Labs 27–28

You can deliver end-to-end AI solutions for real-world industry problems

Proctored Examination & Certification

Certified Applied Artificial Intelligence and Natural Language Processing Expert

You can train, fine-tune, and deploy language models end-to-end
You can build and run your own local LLM solutions with Ollama and Hugging Face
You can deliver production AI dashboards and pipelines for real industries

Assessment

Proctored Exam

Duration

4 Hours

Questions

150

Pass Mark

75%

Certificate

For Life

This course builds directly on Certified Artificial Intelligence and Machine Learning Professional (C|AIMLP™). Next step: Either tackle Python programming (if needed) with the C|PP™ or move onto AMBSIF™ - the strategic framework for leaders deciding how to implement AI across their organisation.