In this course, learners will build a well-rounded foundation in artificial intelligence – from core AI theory and human-computer interaction to practical development, architecture, and explainability. Learners will explore types of AI, cognitive models, and computer vision, and develop AI solutions using Python and frameworks including TensorFlow, Keras, Microsoft Cognitive Toolkit (CNTK), Apache Spark, and Amazon Machine Learning. They will progress through AI developer and practitioner roles, learning to implement, optimize, and tune AI solutions, and apply AI to domains such as robotics and intelligent information systems. Learners will also examine enterprise AI planning, reusable architecture patterns, and explainable AI principles. In the deep learning track, they will implement neural networks, apply hyperparameter tuning, and tackle regression, classification, and image classification tasks through hands-on labs — including real-world datasets on galaxies, COVID-19, and breast cancer predictions — before interpreting model outputs using SHAP and LIME.