This course provides a comprehensive introduction to the field of machine learning, covering fundamental concepts, techniques, and algorithms. Students will learn to analyze data, identify patterns, and make data-driven predictions and decisions using machine learning models. The course covers supervised and unsupervised learning, including key topics such as regression, classification, clustering, and dimensionality reduction. Practical applications and real-world examples will be emphasized throughout the course to illustrate the power and versatility of machine learning in various domains.
By the end of the course, students will have a solid understanding of core machine-learning principles. They will be equipped with the skills needed to implement and evaluate machine-learning models using popular programming tools and libraries.