Focus on responsible AI: bias detection, fairness, alignment, interpretability, and adversarial robustness.
Master Python fundamentals and essential libraries for ML including NumPy, pandas, and scikit-learn.
Supervised and unsupervised learning, model evaluation, feature engineering, and real-world ML pipelines.
Bias detection, fairness, interpretability, adversarial robustness, AI alignment, and responsible AI practices.
Master LLM architecture, prompt engineering, RAG, fine-tuning, agents, and production deployment.
Advanced prompting techniques: chain-of-thought, few-shot, constitutional AI, and systematic evaluation.