Trees, SVM and Unsupervised Learning

"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of each. The course will also cover how and when to apply them to different scenarios, including binary classification and K > 2 classes. Additionally, you will gain valuable experience in generating data representations through PCA and clustering. With a focus on practical, real-world applications, this course is a valuable asset for anyone looking to upskill or move into the field of data science. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder

• Describe the advantages and disadvantages of trees, and how and when to use them
• Apply SVMs for binary classification or K > 2 classes
• Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work