4.7
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135 个评分

Mark J Grover 另外 +1 位授课教师

8,735 人已注册

提供方

IBM

课程信息

31,871 次近期查看

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.

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

中级

完成时间大约为13 小时

英语（English）

字幕：英语（English）

您将获得的技能

Dimensionality Reduction
Unsupervised Learning
Cluster Analysis
K Means Clustering
Principal Component Analysis (PCA)

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

中级

完成时间大约为13 小时

英语（English）

字幕：英语（English）

授课教师

授课教师评分

4.74/5 （47 个评分）

Mark J Grover

Digital Content Delivery Lead

IBM Data & AI Learning

36,378 个学生

13 门课程

Miguel Maldonado

Machine Learning Curriculum Developer

Data and AI Learning

23,088 个学生

5 门课程

提供方

IBM

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

授课大纲 - 您将从这门课程中学到什么

周

周 1

完成时间为 3 小时

Introduction to Unsupervised Learning and K Means

This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.

完成时间为 3 小时

11 个视频（总计 62 分钟）, 1 个阅读材料, 4 个测验

11 个视频

Course Introduction1分钟

Introduction to Unsupervised Learning - Part 18分钟

Introduction to Unsupervised Learning - Part 24分钟

Introduction to Clustering1分钟

K-Means - Part 13分钟

K-Means - Part 23分钟

K-Means - Part 34分钟

K-Means - Part 44分钟

K Means Notebook - Part 19分钟

K Means Notebook - Part 26分钟

K Means Notebook - Part 313分钟

1 个阅读材料

Summary10分钟

3 个练习

Introduction to Unsupervised Learning10分钟

K Means Clustering10分钟

End of Module20分钟

周

周 2

完成时间为 5 小时

Selecting a clustering algorithm

In this module you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.

完成时间为 5 小时

16 个视频（总计 143 分钟）, 1 个阅读材料, 6 个测验

16 个视频

Distance Metrics - Part 13分钟

Distance Metrics - Part 26分钟

Curse of Dimensionality Notebook - Part 111分钟

Curse of Dimensionality Notebook - Part 212分钟

Curse of Dimensionality Notebook - Part 312分钟

Curse of Dimensionality Notebook - Part 410分钟

Hierarchical Agglomerative Clustering - Part 13分钟

Hierarchical Agglomerative Clustering - Part 29分钟

DBSCAN - Part 15分钟

DBSCAN - Part 28分钟

Mean Shift9分钟

Comparing Algorithms11分钟

Clustering Notebook - Part 114分钟

Clustering Notebook - Part 26分钟

Clustering Notebook - Part 37分钟

Clustering Notebook - Part 411分钟

1 个阅读材料

Summary10分钟

4 个练习

Distance Metrics10分钟

Clustering Algorithms10分钟

Comparing Clustering Algorithms10分钟

End of Module10分钟

周

周 3

完成时间为 5 小时

Dimensionality Reduction

This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data. At the end of this module, you will have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.

完成时间为 5 小时

9 个视频（总计 88 分钟）, 2 个阅读材料, 5 个测验

9 个视频

Dimensionality Reduction - Part 114分钟

Dimensionality Reduction - Part 26分钟

PCA Notebook - Part 111分钟

PCA Notebook - Part 212分钟

PCA Notebook - Part 311分钟

Non Negative Matrix Factorization9分钟

Non Negative Matrix Factorization Notebook - Part 18分钟

Non Negative Matrix Factorization Notebook - Part 26分钟

Dimensionality Reduction Imaging Example7分钟

2 个阅读材料

Non Negative Matrix Factorization (Activity)2分钟

Summary10分钟

3 个练习

Dimensionality Reduction5分钟

Non Negative Matrix Factorization10分钟

End of Module10分钟

审阅

4.7

30 条评论

5 stars
81.81%
4 stars
12.58%
3 stars
2.79%
2 stars
2.09%
1 star
0.69%

来自 UNSUPERVISED MACHINE LEARNING的热门评论

由 AV 提供Jul 5, 2021

Great course. Maybe there is one instance of wrong answer in one of the quizzes. Everything elese is perfect. Thanks IBM !

由 MK 提供Feb 21, 2022

Thank you Coursera. Thank you IBM.\n\nThank you to all instructors.

由 AD 提供Apr 18, 2021

It is a beautifully crafted course that looks at various clustering algorithms. More importantly, show the pros and cons of each algorithm/technique based on different patterns.

由 KD 提供Apr 11, 2022

Awesome and wholesome explaination of the concepts

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Unsupervised Machine Learning

课程信息

您将获得的技能