Unsupervised Machine Learning

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Unsupervised Machine Learning

This course is part of multiple programs.

Instructors: Mark J Grover +3 more

48,633 already enrolled

Included with

7 modules

Gain insight into a topic and learn the fundamentals.

365 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

7 modules

Gain insight into a topic and learn the fundamentals.

365 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

Skills you'll gain

Category: Data Preprocessing
Category: Machine Learning Methods
Category: Machine Learning
Category: Model Evaluation
Category: Big Data
Category: Algorithms
Category: Dimensionality Reduction
Category: Unsupervised Learning
Category: Machine Learning Algorithms
Category: Applied Machine Learning
Category: Text Mining

Tools you'll learn

Category: Scikit Learn (Machine Learning Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments¹

AI Graded see disclaimer

Taught in English

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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 videosTotal 62 minutes

Course Introduction1 minute
Introduction to Unsupervised Learning: Overview8 minutes
Introduction to Unsupervised Learning: Use Cases of Clustering5 minutes
Introduction to Clustering1 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-means5 minutes
(Optional) K Means Notebook - Part 19 minutes
K Means Notebook - Part 27 minutes
(Optional) K Means Notebook - Part 313 minutes

2 readingsTotal 20 minutes

Mixture of Gaussians 10 minutes
Summary10 minutes

3 assignmentsTotal 40 minutes

Ungraded: Introduction to Unsupervised Learning10 minutes
Ungraded: K Means Clustering10 minutes
Graded: Module 1 Quiz20 minutes

3 app itemsTotal 105 minutes

K Means Demo (Activity)30 minutes
Practice Lab: K Means Clustering Lab30 minutes
Practice Lab: Mixture of Gaussians Lab45 minutes

What's included

6 videos1 reading2 assignments2 app items

6 videosTotal 57 minutes

Distance Metrics: Euclidean and Manhattan Distance4 minutes
Distance Metrics: Cosine and Jaccard Distance 6 minutes
Curse of Dimensionality Notebook - Part 112 minutes
Curse of Dimensionality Notebook - Part 212 minutes
Curse of Dimensionality Notebook - Part 312 minutes
Curse of Dimensionality Notebook - Part 410 minutes

1 readingTotal 10 minutes

Summary10 minutes

2 assignmentsTotal 30 minutes

Ungraded: Distance Metrics10 minutes
Graded: Module 2 Quiz20 minutes

2 app itemsTotal 90 minutes

Demo lab: Curse of Dimensionality45 minutes
Practice Lab: Distance Metrics Lab45 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 videosTotal 86 minutes

Hierarchical Agglomerative Clustering 4 minutes
Hierarchical Agglomerative Clustering: Hierarchical Linkage Types7 minutes
Applying Hierarchical Agglomerative Clustering 2 minutes
DBSCAN 5 minutes
Visualizing DBSCAN 9 minutes
Mean Shift9 minutes
Comparing Algorithms12 minutes
Clustering Notebook - Part 114 minutes
Clustering Notebook - Part 26 minutes
(Optional) Clustering Notebook - Part 37 minutes
Clustering Notebook - Part 411 minutes

1 readingTotal 10 minutes

Summary10 minutes

3 assignmentsTotal 40 minutes

Ungraded: Clustering Algorithms10 minutes
Ungraded: Comparing Clustering Algorithms10 minutes
Graded: Module 3 Quiz20 minutes

3 app itemsTotal 120 minutes

Practice lab: DBSCAN Clustering30 minutes
Practice lab: Mean Shift Clustering30 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 videosTotal 46 minutes

Dimensionality Reduction: Overview5 minutes
Dimensionality Reduction: Principal Component Analysis9 minutes
(Optional) Dimensionality Reduction Notebook - Part 111 minutes
Dimensionality Reduction Notebook - Part 213 minutes
Dimensionality Reduction Imaging Example8 minutes

1 readingTotal 10 minutes

Summary10 minutes

2 assignmentsTotal 30 minutes

Ungraded: Dimensionality Reduction10 minutes
Graded: Module 4 Quiz20 minutes

4 app itemsTotal 165 minutes

(Optional) Matrix Review45 minutes
Demo lab: Dimensionality Reduction (Part 1)30 minutes
Practice lab: Principal Component Analysis45 minutes
Singular Value Decomposition45 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 videosTotal 18 minutes

Kernel Principal Component Analysis and Multidimensional Scaling6 minutes
Dimensionality Reduction Notebook - Part 311 minutes

1 readingTotal 10 minutes

Summary10 minutes

2 assignmentsTotal 30 minutes

Ungraded: Kernel PCA and MDS10 minutes
Graded: Module 5 Quiz20 minutes

3 app itemsTotal 120 minutes

Demo lab: Dimensionality Reduction (Part 2)30 minutes
Practice lab: Kernel PCA45 minutes
Practice lab: Multidimensional Scaling45 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 videosTotal 24 minutes

Non Negative Matrix Factorization9 minutes
Non Negative Matrix Factorization Notebook - Part 19 minutes
Non Negative Matrix Factorization Notebook - Part 26 minutes

1 readingTotal 10 minutes

Summary10 minutes

2 assignmentsTotal 30 minutes

Ungraded: Non Negative Matrix Factorization10 minutes
Graded: Module 6 Quiz20 minutes

3 app itemsTotal 150 minutes

Demo lab: Non-Negative Matrix Factorization60 minutes
(Optional) TF-IDF Supplemental30 minutes
Practice lab: Non-Negative Matrix Factorization60 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 readingsTotal 12 minutes

Final Project Overview10 minutes
Thanks from the Course Team2 minutes

1 peer reviewTotal 15 minutes

Option 2: Peer Graded - Final Project Submission and Evaluation15 minutes

1 app itemTotal 20 minutes

Option 1: AI Graded - Final Project: Submission and Evaluation20 minutes

1 pluginTotal 2 minutes

Reading: Final Submission Guidelines and Deliverables2 minutes

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Instructors

Instructor ratings

(93 ratings)

Mark J Grover

IBM

13 Courses166,698 learners

Miguel Maldonado

IBM

5 Courses126,727 learners

Joseph Santarcangelo

IBM

37 Courses2,461,722 learners

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IBM

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Showing 3 of 365

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 !

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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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.