This course provides an introduction to machine learning for master's students in mathematics, covering both theoretical foundations and practical implementations. Students will learn key concepts in supervised and unsupervised learning, model evaluation, and real-world applications using Python. The course includes flipped classroom sessions, coding assignments, and a capstone project. Additionally, the course features an instructor from the industry and two guest lectures.