Every course sits at the capability level it actually builds toward. Find where you stand, then see what's next.
Take the capability check to see your own level highlighted here.
AI fundamentals, how modern AI works, GenAI basics, prompting, AI use cases, limitations, and responsible AI.
AI, ML and generative AI: what's the difference How modern AI systems actually work A brief tour of real AI use cases
Python fundamentals, data structures, NumPy, Pandas, data handling, basic visualization, and programming for AI.
Python syntax and control flow Functions and data structures Writing clean, readable code
LLMs, prompt engineering, APIs, embeddings, RAG with LangChain, vector databases, AI agents, and building GenAI applications.
How large language models work Tokens, context and limitations Choosing the right model for a task
Supervised/unsupervised learning, feature engineering, model selection, training, evaluation, overfitting, and practical ML projects.
Supervised vs. unsupervised learning Choosing the right approach for a problem Overfitting, underfitting and generalization
Model training/fine-tuning, deployment, APIs, model serving, MLOps fundamentals, monitoring, optimization, and production AI systems.
Training pipelines end to end Fine-tuning pretrained models Working with limited compute and data
A two-minute check tells you more than browsing ever will.
Know your AI capability