Learning Area

You understand how computers work, navigate the terminal confidently, use Git and understand open source.

You program in Python, understand data types, control structures, OOP and set up clean development environments.

You generate your own images with Stable Diffusion, understand text-to-image and master LoRA, ControlNet and upscaling.

You create system prompts, build RAG pipelines, use function calling and automate workflows with n8n.

You understand algorithms, Big-O notation, recursion and the key data structures (linear, hierarchical, key-based).

You understand measures of central tendency, dispersion, distributions, correlation, probability and Bayes' theorem.

You understand vectors, matrices, tensors, derivatives, partial derivatives and the chain rule as the foundation for backpropagation.

You understand the roots of AI, navigate state spaces, master pathfinding and game theory, and know the lessons of the AI winter.

You understand ML paradigms, train supervised learning models, evaluate with metrics and know overfitting and unsupervised learning.

You understand neural networks from the perceptron to backpropagation and can follow the complete training process.

You understand tokenization, embeddings, CNNs, RNNs, transformers, LLMs, transfer learning, sampling and diffusion.

You distinguish AGI from stochastics, understand training bias analytically, know copyright questions and prompt injection risks.

You use AI as an everyday helper, recognize fraud schemes and maintain control over your data.

You use AI as a learning tutor, recognize deepfakes and synthetic media and critically verify sources.

A curated handful of standalone demos — a stroll for the curious. No homework, just exploration.