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Learner Reviews & Feedback for Introduction to Machine Learning: Supervised Learning by University of Colorado Boulder

3.4
stars
79 ratings

About the Course

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 MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary 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...

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26 - 32 of 32 Reviews for Introduction to Machine Learning: Supervised Learning

By James T

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Nov 13, 2024

Best free MOOC on Coursera for supervised learning.

By vanillaSky

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Apr 10, 2025

the coding problem is sometime vague, not sure what to do

By Yao G

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Mar 24, 2025

This course is terribly taught at a pure knowledge level (basic statistics like t-tests, p-values, etc.), but also makes vague gestures to anti-racism by using racist datasets uncritically???? In particular, the boston dataset is so notorious for its racism that its been removed from a number of industry standard tools. Yet the professor requires the student to use the dataset in exactly the same ways as the dataset is critiqued. https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8 As someone who finished all three of her courses, her courses do not get better over time either.

By Chayse G

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Jul 15, 2025

Poor instruction on the labs. Irrelevant feedback from the auto-grader. ZERO support from TA's and Course Instructors. This course is awful. I am discouraged from even trying to attempt weeks 3 - 6. The issues expressed by students have existed for over 2 years with no resolutions or support.

By Hidetake T

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Feb 12, 2023

good course.

By Juan J C A

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Apr 15, 2025

This course is good if you want a deep understanding of the mathematics and statistics behind the models. However, I think it should include more real-world examples with code, make greater use of existing libraries, and spend less time recreating functions that libraries already provide.

By Pran K M

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Dec 18, 2023

frustrating lecture zero level assignment super level