Machine Learning for Data Science

Catalog description

Machine Learning for Data Science is a foundational course designed to equip students with the essential theoretical and practical Python skills necessary for a career in data science and machine learning. This comprehensive course delves into the fundamentals of statistical learning, addressing key concepts such as the curse of dimensionality, the bias-variance tradeoff, model selection, regularization, bootstrap and uncertainty quantification. Students will gain hands-on experience with a variety of machine learning models including regression and classification trees, ensemble learning, boosting, support vector machines, neural networks, hierarchical clustering and K-means. The curriculum is structured to provide practical Python programming skills from scratch, which are crucial for succeeding in subsequent courses. By applying these techniques to real-world scenarios in finance, business and industry, the course ensures that students not only understand the theory behind machine learning but also how to apply it effectively in professional settings. This course is an indispensable part of the educational journey for aspiring data scientists, laying the groundwork for advanced studies and applications in the field.