This course offers a comprehensive introduction to the core concepts, techniques, and algorithms of machine learning, as well as some platforms commonly used in practice. Students will explore essential topics such as data preprocessing—including data manipulation, transformation, feature selection, and dimensionality reduction— followed by key methods in supervised learning like regression and classification. The course covers unsupervised learning approaches such as clustering and association rule mining. Moreover, Artificial Neural Networks are covered through the study of the perceptron, the multi-layer perceptron. An overview of deep networks and multi-task deep learning is provided. Foundational ideas, principles and applications of Reinforcement Learning are also covered. Throughout the course, students will not only develop a solid understanding of the theoretical underpinnings of these algorithms but also acquire practical skills in implementing data workflows, applying machine learning methods to real-world data, and evaluating model performance. Applications across diverse domains are discussed to illustrate the impact and versatility of machine learning.