Course Description

Applied Artificial Intelligence and Machine Learning is a practical, hands-on course designed to introduce learners to the core concepts and real-world applications of Artificial Intelligence (AI) and Machine Learning (ML). The course begins with the foundations of Python programming, enabling participants to work with essential libraries used in data analysis and machine learning. Learners will gain experience in data preprocessing, exploratory data analysis, feature engineering, and building predictive models using popular machine learning algorithms. The course further covers important techniques such as model evaluation, cross-validation, dimensionality reduction, and hyperparameter tuning to improve model performance. In addition, students will be introduced to the basics of Deep Learning, including neural networks, activation functions, and the fundamentals of building simple deep learning models. Through hands-on exercises, practical implementations, and real-world case studies, participants will learn how to develop intelligent systems that can analyze data, make predictions, and support data-driven decision-making. By the end of the course, learners will have the foundational knowledge and practical skills required to build and evaluate machine learning and basic deep learning models using Python.