Artificial Intelligence & Machine Learning

What is the difference between AI, ML, and Deep Learning?

AI (Artificial Intelligence) is the broad field aimed at simulating intelligent behavior. Machine Learning (ML) is a subset of AI where systems learn from data without being explicitly programmed. Deep Learning is a subset of ML that uses deep neural networks, particularly effective on images, text, and audio.

What is feature engineering?

Feature engineering is the process of creating, selecting, and transforming input variables (features) to improve model performance. It is often the step with the highest impact on results, even before choosing an algorithm. It includes handling missing values, encoding, normalization, and creating derived variables.

How do you evaluate a ML model?

Evaluation depends on the problem type. For classification: precision, recall, F1-score, AUC-ROC. For regression: RMSE, MAE, R². Always evaluate on a test set unseen during training and monitor overfitting. Cross-validation is recommended on small datasets.

What is responsible AI?

Responsible AI is a set of principles and practices aimed at making AI systems reliable, fair, transparent, explainable, and privacy-respecting. It includes bias detection and correction, decision explainability (XAI), regulatory compliance (EU AI Act), and defining human oversight processes.

Is the AI Fundamentals deck really free?

Yes. The Artificial Intelligence Fundamentals deck (35 cards) is offered free of charge to let any professional try the approach before subscribing. The 5 other decks in this domain are premium.