What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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Instructors: Mark J Grover
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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.
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.
11 videos2 readings3 assignments3 app items
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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.
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.
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.
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This module introduces matrix factorization, which is a powerful technique for big data, text mining, and pre-processing data.
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Now, you have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.
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We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on Feb 21, 2023
Excellent course for me! I had a lot of "Ah ha!" moments during the course! Phenomenal!
Reviewed on 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.
Reviewed on Jul 5, 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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