Certified Applied AI & NLP Expert

Set up an advanced AI environment and cover core NLP concepts such as tokens, embeddings, transformers and attention.

• What generative AI is
• Tokens and embeddings
• Transformers and attention
• LLaMA 3, Mistral and API setup
• Preprocessing and normalisation
• Lab 0

You will be able to: Set up advanced AI/NLP tools and explain transformer architectures.

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

• Tokenisation and embeddings
• RNNs and sequential data
• Word2Vec and GloVe
• Handling noisy or missing data
• TF-IDF feature engineering
• Labs 1, 2, 7, 8 and 9

You will be able to: Build foundational NLP pipelines using embeddings and feature engineering.

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

• Advanced fine-tuning
• Instruction-tuning
• Domain adaptation
• LoRA and adapters
• Parameter-efficient methods
• Labs 3, 4, 5 and 10A to 10C

You will be able to: Fine-tune and train local LLMs for domain-specific tasks.

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

• Custom task prompts
• Chain-of-thought reasoning
• Zero-shot and few-shot prompting
• Multi-shot prompts
• Evaluation and deployment

You will be able to: Engineer prompts that produce accurate, reliable LLM outputs.

Extract entities such as people, places and organisations, with real-world applications and ethical considerations.

• Entity recognition models
• NER applications
• Steps and challenges
• Ethics in NER
• Lab 6 (Python and C#)

You will be able to: Extract and classify named entities from text data.

Build translation pipelines and summarise across languages using GPT (latest models), Gemini and LLaMA 3.

• Machine translation
• Translation challenges
• Multilingual summarisation
• Translation pipelines
• Real-world applications
• Labs 11 to 14

You will be able to: Build multilingual translation and summarisation systems.

Text-to-image, image-to-text and cross-modal models (Gemini, BLIP, Grok) for industry use.

• What multimodal generation is
• Gemini, BLIP and Grok
• Industry applications
• Multimodal pipelines
• Ethical challenges
• Lab 15

You will be able to: Generate and process multimodal content across text and images.

Deploy AI models through web, Python and C# integrations, with deployment workflows, case studies and ethical practice.

• Deployment workflow
• Web integration
• Python and C# deployment
• Case studies
• Ethics in deployment
• Labs 16 to 18

You will be able to: Deploy LLMs to web, Python and C# applications.

Interactive dashboards using Streamlit, Dash and Blazor, connected to LLMs and live data.

• Streamlit, Dash and Blazor
• Dashboard components
• Connecting LLMs
• Sector use cases
• Best practices
• Labs 19 and 20

You will be able to: Build AI-powered dashboards that connect LLMs to real data.

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

• NLP pipelines
• Multimodal pipelines
• Real-time processing
• Deployment and monitoring

You will be able to: Architect complete AI pipelines from ingestion to production.

Advanced deployment, including web app integration, Python and C# API integration, and case studies at scale.

• Advanced deployment workflow
• Web app integration
• Python and C# API integration
• Case studies at scale

You will be able to: Integrate AI models into production applications across platforms.

Advanced dashboard development and sector-specific use cases.

• Advanced dashboard components
• Development frameworks
• Model-to-dashboard connections
• Cybersecurity dashboards
• Finance and healthcare
• Labs 21 to 26

You will be able to: Build sector-specific multimodal dashboards with live LLM integration.

A capstone integration covering real-time threat detection, financial forecasting pipelines, monitoring, scaling and evaluation.

• Real-time threat detection
• Financial forecasting pipeline
• Monitoring and scaling
• Evaluation and governance
• Labs 27 and 28

You will be able to: Deliver end-to-end AI solutions for real-world industry problems.