学生对 IBM 提供的 Unsupervised Machine Learning 的评价和反馈

4.7

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358 个评分

课程概述

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....

51 - Unsupervised Machine Learning 的 59 个评论（共 59 个）

创建者 Poonam K

•

Jan 9, 2025

good

创建者 SHUBHAM K

•

Jun 3, 2022

good

创建者 MUHAMMAD E B

•

Nov 24, 2025

JOS

创建者 Maram A A

•

Dec 28, 2022

👍

创建者 Rajiv s

•

May 7, 2025

创建者 Cedric K

•

Jul 2, 2024

Not 5 stars. Why? First of all, many videos of demo labs need to be updated to match the notebooks. For the most part, the content in the notebooks differs (ever so slightly) from what is in the watch along videos. Secondly, some graded & ungraded assignments are just plain wrong. The "correct" answers are actually incorrect. I found these two issues rather irritating.