Name: Introduction to Machine Learning: Supervised Learning
Brand: University of Colorado Boulder
Availability: OnlineOnly
Rating: 3.3 (34 reviews)

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学生对 University of Colorado Boulder 提供的 Introduction to Machine Learning: Supervised Learning 的评价和反馈

3.3

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82 个评分

课程概述

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://hua.dididi.sbs/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder...

26 - Introduction to Machine Learning: Supervised Learning 的 34 个评论（共 34 个）

创建者 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.

创建者 Pran K M

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Dec 18, 2023

frustrating lecture zero level assignment super level

创建者 MOOCStudentTrial2

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Aug 16, 2024

Labs need to be more clear

创建者 James T

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Nov 12, 2024

Best free MOOC on Coursera for supervised learning.

创建者 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.

创建者 vanillaSky

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Apr 10, 2025

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

创建者 Hidetake T

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Feb 11, 2023

good course.

创建者 Lindsy

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Sep 12, 2025

So far, I have to say this course is terrible! Very hard to understand presenter. The printed words often do not match what the presenter says - making it much more difficult in trying to understand what she is actually saying! The programming assignments are a mess, with unclear instructions and things not even discussed previously. On top of this discombobulated course...Jupyter is a pain too - constant issues! Very disappointed.