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Introduction to Machine Learning: Supervised Learning に戻る

コロラド大学ボルダー校(University of Colorado Boulder) による Introduction to Machine Learning: Supervised Learning の受講者のレビューおよびフィードバック



In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. 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



Introduction to Machine Learning: Supervised Learning: 1 - 7 / 7 レビュー

by Mahmudul H


This was an excellent introductory course that allowed me to get into the world of Data Science and Machine Learning.

by Nathan H


T​he auto-graded assignments in this course offer much better feedback than some of the other CU Boulder MS-DS courses that I've taken but they still have issues with confusing, incomplete, or incorrect instructions and cryptic feedback.

T​here's a lot of good material in the course. The coverage seems pretty basic, but that's fine. The last section (i.e. week) which deals with support vector machines doesn't hold together as well as the rest of the course.

T​he course contains peer graded assignments which are fine in principle, but it seems like Coursera will only let me do the required "grading" part of them when the deadline gets close. That interacts poorly with the due date resets and means that the course isn't really self-paced. I also received a non-passing grade on a module three hours before the due date closed it off when I had submitted it a month before.

by Zehu C


the course is comprehensive and rigorous and provides good exercise with the assignment. But the lecture is not clear enough with a new concept and didn't really provide a good example explaining them. And the auto-grade assignment is difficult to finish because the instruction is not clear and the lecture didn't provide much on how to do the assignment.

by Mario A h C


I'm not sure why it did not click for me.

Perhaps too independent for me. It would be great if the videos share more code and how to use the tools and resources offered.


by Vishal P


I would not recemmend this course. I was looking for a course where the instructor to teach concepts and provides examples. The course is designed around on reading and the lecture does a quick overview of what is read and doesn't do justice.

by Pratik P


The course is misleading, the python part is completely neglected and the assignments is not properly decribed to be able to perform. The theory can be found in any statistics courses and books. Implementation is a huge issue to most.

by Ashish R


This is one of the worst ML courses out there. So many mistakes and virtually inactive discussion forum. DO NOT TAKE THIS COURSE !