Unsupervised Machine Learning

Unsupervised Machine Learning

This course is part of multiple programs.

Instructors: Mark J Grover

47,292 already enrolled

7 modules

Gain insight into a topic and learn the fundamentals.

361 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

7 modules

Gain insight into a topic and learn the fundamentals.

361 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

Skills you'll gain

Tools you'll learn

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 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

Ungraded: Introduction to Unsupervised Learning 10 minutes
Ungraded: K Means Clustering 10 minutes
Graded: Module 1 Quiz 20 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

Ungraded: Distance Metrics 10 minutes
Graded: Module 2 Quiz 20 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

Ungraded: Clustering Algorithms 10 minutes
Ungraded: Comparing Clustering Algorithms 10 minutes
Graded: Module 3 Quiz 20 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

Ungraded: Dimensionality Reduction 10 minutes
Graded: Module 4 Quiz 20 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

Ungraded: Kernel PCA and MDS 10 minutes
Graded: Module 5 Quiz 20 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

Ungraded: Non Negative Matrix Factorization 10 minutes
Graded: Module 6 Quiz 20 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.