The course "Artificial Intelligence (AI) and Machine Learning – Exam" provides an in-depth overview of key concepts and methodologies in AI and machine learning. Starting from fundamental learning paradigms such as supervised, unsupervised, semi-supervised, and reinforcement learning, the course covers data preparation including cleaning, feature engineering, and normalization. It examines major machine learning algorithms like regression, classification, clustering, decision trees, ensembles, and SVMs, and explains training and validation pipelines including cross-validation and evaluation metrics (accuracy, precision/recall, F1, AUC, RMSE). Advanced topics such as overfitting, underfitting, regularization, interpretability using tools like SHAP and LIME, and an introduction to neural networks and deep learning are also included. A section is dedicated to MLOps, focusing on data/model versioning and monitoring. Finally, the course addresses ethics, bias, privacy, and security issues related to AI/ML models, preparing participants to successfully pass a professional-level exam.
The pathway is designed for managers, executives, function owners, auditors and professionals. Lessons use validated minimum study time and a contextual tutor; assessments use server-side random selection of questions and answers, session-continuity controls, camera and local audio-activity monitoring during protected tests.
Traceability and reliability: integrity events are logged for authorized review; automated signals do not determine the result on their own.
Purpose, references, delivery method, commercial terms and certificate value, presented clearly and transparently.