IBM

Unsupervised Machine Learning

This course is part of multiple programs.

Taught in English

Some content may not be translated

Mark J Grover
Miguel Maldonado
Joseph Santarcangelo

Instructors: Mark J Grover

25,176 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(247 reviews)

|

94%

Intermediate level
Some related experience required
23 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(247 reviews)

|

94%

Intermediate level
Some related experience required
23 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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There are 7 modules in this course

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.

What's included

11 videos2 readings3 quizzes3 app items

What's included

6 videos1 reading2 quizzes2 app items

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.

What's included

11 videos1 reading3 quizzes3 app items

This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data.

What's included

5 videos1 reading2 quizzes4 app items

This module introduces dimensionality reduction techniques like Kernal Principal Component Analysis and multidimensional scaling. These methods are more powerful than Principal Component Analysis in many applications.

What's included

2 videos1 reading2 quizzes3 app items

This module introduces matrix factorization, which is a powerful technique for big data, text mining, and pre-processing data.

What's included

3 videos1 reading2 quizzes3 app items

Now, you have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.

What's included

1 peer review

Instructors

Instructor ratings
4.6 (69 ratings)
Mark J Grover
IBM
13 Courses91,719 learners
Miguel Maldonado
IBM
5 Courses67,449 learners
Joseph Santarcangelo
IBM
28 Courses1,488,127 learners

Offered by

IBM

Recommended if you're interested in Machine Learning

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Learner reviews

Showing 3 of 247

4.6

247 reviews

  • 5 stars

    77.01%

  • 4 stars

    16.53%

  • 3 stars

    2.82%

  • 2 stars

    1.61%

  • 1 star

    2.01%

NW
5

Reviewed on Dec 25, 2020

MK
5

Reviewed on Feb 21, 2022

KD
5

Reviewed on Apr 11, 2022

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