Climb the AI ladder,
one rung at a time.
10 programs that take you from “what even is AI?” to shipping real projects — unlocked module by module, so you always know exactly what to learn next.
Pick your rung
Every program starts exactly where you are — Class 6 to senior engineer.
Unlock module by module
Finish one module and the next opens. No skipping, no getting lost.
Prove it in a quiz
Short MCQ checks after each module, with retakes when you need them.
Ship a real project
Close with a capstone your trainer grades — then collect your certificate.
Find the rung that fits you
Class 6 — AI Explorer
Main goal: USE AI. Become comfortable with AI and learn to use it responsibly. Emphasis: AI Thinking. Learning outcomes — explain what AI is in plain words and spot AI in everyday apps; ask clear questions (prompts) and judge whether an AI answer might be wrong; use AI tools to write, illustrate and narrate a short story; follow basic AI safety rules: privacy, copyright, spotting deepfakes. Skills learned: AI awareness, basic prompting, creative AI tool use (text, image, voice), responsible and safe use.
Class 7 — AI Creator
Main goal: CREATE WITH AI. Use multiple AI tools together to create things. Emphasis: Prompt Engineering. Learning outcomes — write structured, iterative prompts using context, examples and constraints; combine three or more AI tools (image, voice, video, music) into one output; edit and enhance AI-generated photos using inpainting and outpainting; produce a complete 1-2 minute AI-assisted video, from idea to final edit. Skills learned: prompt engineering, multimodal workflows, AI video and audio production, project planning.
AI Explorer
A hands-on, no-fear first climb into AI — spotting it, training it, questioning it, and building a first mini project.
Class 8 — AI Investigator
Main goal: CODE WITH AI. Stop being only an AI user and start becoming a builder. Emphasis: AI + Critical Thinking. Learning outcomes — read and write basic Python (variables, conditions, loops, functions, lists and dictionaries); use AI to explain, debug and test code rather than copying it blindly; build and explain at least five small Python programs; apply structured problem-solving to break a task into steps before coding it. Skills learned: Python fundamentals, AI-assisted debugging, algorithmic thinking, code documentation.
Class 9 — AI Problem Solver
Main goal: BUILD WITH AI. Understand how AI is used inside real applications. Emphasis: AI + Creativity. Learning outcomes — explain, at a basic level, how large language models, tokens, context windows and embeddings work; call an AI model through an API; build a custom AI assistant with instructions, knowledge and tools; design a simple automated workflow that connects apps and AI together. Skills learned: LLM literacy, API basics, assistant design, no-code automation.
Class 10 — AI Innovator
Main goal: COMBINE EVERYTHING. Combine everything learned into real projects. Emphasis: AI + Real-World Problem Solving. Learning outcomes — use advanced prompting and multimodal workflows and critically evaluate AI output and its limitations; use Git and GitHub for basic version control; build an AI assistant with tool use and a knowledge base, using a basic RAG concept; combine coding, generative AI, assistants and automation into one real-world capstone project; describe how AI is used in business, marketing and data-driven decisions. Skills learned: advanced GenAI workflows, version control, RAG fundamentals, automation at scale, capstone project delivery, business AI awareness.
AI for Everyone
AI literacy for every field of study — using no-code tools and generative AI to think, build and pitch without writing a line of code.
AI Professional
Bring AI into your day job — automate workflows, deploy GenAI copilots, read AI-driven analytics, and pitch a real adoption roadmap.
AI Engineering & Innovation
Advanced ML systems, transformer engineering, production GenAI and MLOps — for engineers building the next AI product, not just a model.
AI Practitioner
A builder's path from Python and statistics to deployed deep learning and GenAI applications — culminating in a shipped capstone.
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