About Introduction to Machine Learning: Supervised Learning course
In this course, you will learn various supervised ML algorithms and forecasting tasks applied to different data. You will learn when to use each model and why, as well as how to improve model performance. We will cover models such as linear and logistic regression, KNN, decision trees, and ensemble methods such as Random Forest and Boosting, kernel methods such as SVM. Prior knowledge of coding or scripting is required. We will use Python extensively throughout the course. To take this course, you will need a solid foundation in Python or enough previous coding experience in other programming languages to quickly master Python.
We’ll learn how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. This course is designed for programmers who are new to these libraries. Previous experience with these libraries is helpful but not required. College-level math skills, including calculus and linear algebra, are required. We hope you’ll find the math clear but not intimidating. This course can be taken for academic credit toward CU Boulder’s MS in Data Science or MS in Computer Science programs, both offered on Coursera. These fully accredited degrees offer focused courses, short 8-week sessions, and paid tuition. Admission is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
