This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts: Part I covers the fundamentals of machine learning, and, Part II demonstrates the applications of various machine methods in solving a real world problem. Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration. Level: Introductory to Intermediate Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Alliance Account Data preparation or equivalent knowledge. Basic Python knowledge and experience. Knowledge and experience with Tensorflow and Scikit-learn would also be helpful. (part of the 2024 Compute Ontario Summer School)