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.
Unsupervised Machine Learning
Unsupervised Machine Learning
This course is part of multiple programs.
Instructors: Mark J Grover
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4.7
(281 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 23 hours
Learn at your own pace
95%
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Assessments
14 assignments
Taught in English
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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 assignments3 app items
What's included
6 videos1 reading2 assignments2 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 assignments3 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 assignments4 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 assignments3 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 assignments3 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 reading1 peer review
Instructors
Instructor ratings
4.6 (74 ratings)Offered by
Recommended if you're interested in Machine Learning
Duke University
University of Colorado Boulder
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Politecnico di Milano
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4.7
281 reviews
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Showing 3 of 281
TT
5
Reviewed on Feb 22, 2023
Excellent course for me! I had a lot of "Ah ha!" moments during the course! Phenomenal!
AD
5
Reviewed on Apr 19, 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.
VA
5
Reviewed on Jul 6, 2021
Great course. Maybe there is one instance of wrong answer in one of the quizzes. Everything elese is perfect. Thanks IBM !
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