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Unsupervised Machine Learning

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Unsupervised Machine Learning

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Gain insight into a topic and learn the fundamentals.
4.7

367 reviews

Intermediate level
Some related experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
95%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

367 reviews

Intermediate level
Some related experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
95%
Most learners liked this course

Build your subject-matter expertise

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There are 7 modules in this course

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.

What's included

11 videos2 readings3 assignments3 app items

11 videos•Total 62 minutes
  • Course Introduction•1 minute
  • Introduction to Unsupervised Learning: Overview•8 minutes
  • Introduction to Unsupervised Learning: Use Cases of Clustering•5 minutes
  • Introduction to Clustering•1 minute
  • K-Means •4 minutes
  • K-Means Initialization •4 minutes
  • Selecting the Right Number of Clusters in K-Means •5 minutes
  • Elbow method and Applying K-means•5 minutes
  • (Optional) K Means Notebook - Part 1•9 minutes
  • K Means Notebook - Part 2•7 minutes
  • (Optional) K Means Notebook - Part 3•13 minutes
2 readings•Total 20 minutes
  • Mixture of Gaussians •10 minutes
  • Summary•10 minutes
3 assignments•Total 40 minutes
  • Graded: Module 1 Quiz•20 minutes
  • Ungraded: Introduction to Unsupervised Learning•10 minutes
  • Ungraded: K Means Clustering•10 minutes
3 app items•Total 105 minutes
  • K Means Demo (Activity)•30 minutes
  • Practice Lab: K Means Clustering Lab•30 minutes
  • Practice Lab: Mixture of Gaussians Lab•45 minutes

What's included

6 videos1 reading2 assignments2 app items

6 videos•Total 57 minutes
  • Distance Metrics: Euclidean and Manhattan Distance•4 minutes
  • Distance Metrics: Cosine and Jaccard Distance •6 minutes
  • Curse of Dimensionality Notebook - Part 1•12 minutes
  • Curse of Dimensionality Notebook - Part 2•12 minutes
  • Curse of Dimensionality Notebook - Part 3•12 minutes
  • Curse of Dimensionality Notebook - Part 4•10 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
2 assignments•Total 30 minutes
  • Graded: Module 2 Quiz•20 minutes
  • Ungraded: Distance Metrics•10 minutes
2 app items•Total 90 minutes
  • Demo lab: Curse of Dimensionality•45 minutes
  • Practice Lab: Distance Metrics Lab•45 minutes

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

11 videos•Total 86 minutes
  • Hierarchical Agglomerative Clustering •4 minutes
  • Hierarchical Agglomerative Clustering: Hierarchical Linkage Types•7 minutes
  • Applying Hierarchical Agglomerative Clustering •2 minutes
  • DBSCAN •5 minutes
  • Visualizing DBSCAN •9 minutes
  • Mean Shift•9 minutes
  • Comparing Algorithms•12 minutes
  • Clustering Notebook - Part 1•14 minutes
  • Clustering Notebook - Part 2•6 minutes
  • (Optional) Clustering Notebook - Part 3•7 minutes
  • Clustering Notebook - Part 4•11 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
3 assignments•Total 40 minutes
  • Graded: Module 3 Quiz•20 minutes
  • Ungraded: Clustering Algorithms•10 minutes
  • Ungraded: Comparing Clustering Algorithms•10 minutes
3 app items•Total 120 minutes
  • Practice lab: DBSCAN Clustering•30 minutes
  • Practice lab: Mean Shift Clustering•30 minutes
  • Clustering Demo (Activity)•60 minutes

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

5 videos•Total 46 minutes
  • Dimensionality Reduction: Overview•5 minutes
  • Dimensionality Reduction: Principal Component Analysis•9 minutes
  • (Optional) Dimensionality Reduction Notebook - Part 1•11 minutes
  • Dimensionality Reduction Notebook - Part 2•13 minutes
  • Dimensionality Reduction Imaging Example•8 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
2 assignments•Total 30 minutes
  • Graded: Module 4 Quiz•20 minutes
  • Ungraded: Dimensionality Reduction•10 minutes
4 app items•Total 165 minutes
  • (Optional) Matrix Review•45 minutes
  • Demo lab: Dimensionality Reduction (Part 1)•30 minutes
  • Practice lab: Principal Component Analysis•45 minutes
  • Singular Value Decomposition•45 minutes

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

2 videos•Total 18 minutes
  • Kernel Principal Component Analysis and Multidimensional Scaling•6 minutes
  • Dimensionality Reduction Notebook - Part 3•11 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
2 assignments•Total 30 minutes
  • Graded: Module 5 Quiz•20 minutes
  • Ungraded: Kernel PCA and MDS•10 minutes
3 app items•Total 120 minutes
  • Demo lab: Dimensionality Reduction (Part 2)•30 minutes
  • Practice lab: Kernel PCA•45 minutes
  • Practice lab: Multidimensional Scaling•45 minutes

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

3 videos•Total 24 minutes
  • Non Negative Matrix Factorization•9 minutes
  • Non Negative Matrix Factorization Notebook - Part 1•9 minutes
  • Non Negative Matrix Factorization Notebook - Part 2•6 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
2 assignments•Total 30 minutes
  • Graded: Module 6 Quiz•20 minutes
  • Ungraded: Non Negative Matrix Factorization•10 minutes
3 app items•Total 150 minutes
  • Demo lab: Non-Negative Matrix Factorization•60 minutes
  • (Optional) TF-IDF Supplemental•30 minutes
  • Practice lab: Non-Negative Matrix Factorization•60 minutes

You now have all the necessary tools to demonstrate your unsupervised learning skills in your final project. Build and compare different models and clearly document each step along with the key insights and findings.

What's included

2 readings1 peer review1 app item1 plugin

2 readings•Total 12 minutes
  • Final Project Overview•10 minutes
  • Thanks from the Course Team•2 minutes
1 peer review•Total 15 minutes
  • Option 2: Peer Graded - Final Project Submission and Evaluation•15 minutes
1 app item•Total 20 minutes
  • Option 1: AI Graded - Final Project: Submission and Evaluation•20 minutes
1 plugin•Total 2 minutes
  • Reading: Final Submission Guidelines and Deliverables•2 minutes

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Instructors

Instructor ratings
4.6 (93 ratings)
IBM
13 Courses•168,824 learners
IBM
5 Courses•128,253 learners
IBM
37 Courses•2,497,133 learners

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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.

VA
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Reviewed on Jul 5, 2021

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

AF
·

Reviewed on Nov 6, 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

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