No Code. No Limits. 

Foundations of AI
Understand what AI truly is, its importance, and where it’s applied.

• What is AI and why it matters

• AGI and human-like intelligence

• AI vs ML vs DL

• Common AI domains and tasks

• Objective: Build conceptual clarity and differentiate between AI, ML, and DL

Machine Learning Essentials
Learn how machines learn, predict, and improve.

• Supervised, Unsupervised, and Reinforcement Learning

• Regression and Classification

• Model lifecycle: from data to deployment

• Hands-on with Jupyter Notebooks

• Objective: Understand core ML types, apply regression models, and recognise labelled vs unlabelled data

Generative AI & Large Language Models
Explore how tools like ChatGPT and Gemini work under the hood.

• What are LLMs and how they generate text

• Transformer architecture: Encoders & Decoders

• Prompt Engineering & Fine-Tuning

• Vector Databases and Retrieval Systems

• Objective: Learn how LLMs create, store, and recall information through RAG and embeddings

Prompt Engineering & Responsible AI
Master prompts that get accurate outputs and understand AI ethics.

• Writing effective prompts

• Responsible AI principles

• Using tools like Perplexity, DALL·E, and GPTs

• Reinforcement Learning from Human Feedback (RLFH)

• Objective: Apply ethical frameworks while experimenting with AI tools

Agentic AI & Automation
Build your first AI agent and connect it to tools.

• What is Agentic AI

• How agents differ from traditional AI

• Frameworks: LangChain, AutoGPT, CrewAI

• Hands-on with n8n for automation

• Objective: Create a workflow that connects GPT-4, Claude, Gemini, and real tools via APIs

Advanced Concepts
Go behind the scenes of modern AI systems.

• Model Context Protocol (MCP): How AI systems communicate

• Retrieval Augmented Generation (RAG)

• Evaluating model accuracy and performance

• Objective: Learn to assess, improve, and optimise GenAI systems