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Unsupervised Machine Learning
  • Level Professional
  • Duration 23 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Introduction to Unsupervised Learning

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Ungraded: Introduction to Unsupervised Learning

4 Videos

  • Course Introduction
  • Introduction to Unsupervised Learning: Overview
  • Introduction to Unsupervised Learning: Use Cases of Clustering
  • Introduction to Clustering

K Means Clustering

    • 7 Videos
    • 1 Readings
    • 3 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: K Means Clustering

3 External Tool

  • K Means Demo (Activity)
  • Practice Lab: K Means Clustering Lab
  • Practice Lab: Mixture of Gaussians Lab

7 Videos

  • K-Means
  • K-Means Initialization
  • Selecting the Right Number of Clusters in K-Means
  • Elbow method and Applying K-means
  • (Optional) K Means Notebook - Part 1
  • K Means Notebook - Part 2
  • (Optional) K Means Notebook - Part 3

1 Readings

  • Mixture of Gaussians

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded: Module 1 Quiz

1 Readings

  • Summary

Computational hurdles of clustering algorithms

    • 6 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Distance Metrics

2 External Tool

  • Demo lab: Curse of Dimensionality
  • Practice Lab: Distance Metrics Lab

6 Videos

  • Distance Metrics: Euclidean and Manhattan Distance
  • Distance Metrics: Cosine and Jaccard Distance
  • Curse of Dimensionality Notebook - Part 1
  • Curse of Dimensionality Notebook - Part 2
  • Curse of Dimensionality Notebook - Part 3
  • Curse of Dimensionality Notebook - Part 4

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded: Module 2 Quiz

1 Readings

  • Summary

Common clustering algorithms

    • 6 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Clustering Algorithms

2 External Tool

  • Practice lab: DBSCAN Clustering
  • Practice lab: Mean Shift Clustering

6 Videos

  • Hierarchical Agglomerative Clustering
  • Hierarchical Agglomerative Clustering: Hierarchical Linkage Types
  • Applying Hierarchical Agglomerative Clustering
  • DBSCAN
  • Visualizing DBSCAN
  • Mean Shift

Comparing clustering algorithms

    • 5 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Comparing Clustering Algorithms

1 External Tool

  • Clustering Demo (Activity)

5 Videos

  • Comparing Algorithms
  • Clustering Notebook - Part 1
  • Clustering Notebook - Part 2
  • (Optional) Clustering Notebook - Part 3
  • Clustering Notebook - Part 4

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Graded: Module 3 Quiz

1 Readings

  • Summary

Dimensionality Reduction

    • 5 Videos
    • 4 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Dimensionality Reduction

4 External Tool

  • (Optional) Matrix Review
  • Demo lab: Dimensionality Reduction (Part 1)
  • Practice lab: Principal Component Analysis
  • Singular Value Decomposition

5 Videos

  • Dimensionality Reduction: Overview
  • Dimensionality Reduction: Principal Component Analysis
  • (Optional) Dimensionality Reduction Notebook - Part 1
  • Dimensionality Reduction Notebook - Part 2
  • Dimensionality Reduction Imaging Example

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded: Module 4 Quiz

1 Readings

  • Summary

Kernel Principal Component Analysis and Multidimensional Scaling

    • 2 Videos
    • 3 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Kernel PCA and MDS

3 External Tool

  • Demo lab: Dimensionality Reduction (Part 2)
  • Practice lab: Kernel PCA
  • Practice lab: Multidimensional Scaling

2 Videos

  • Kernel Principal Component Analysis and Multidimensional Scaling
  • Dimensionality Reduction Notebook - Part 3

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded: Module 5 Quiz

1 Readings

  • Summary

Matrix Factorization

    • 3 Videos
    • 3 ExternalTool
    • 1 Assignment
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1 Assignment

  • Ungraded: Non Negative Matrix Factorization

3 External Tool

  • Demo lab: Non-Negative Matrix Factorization
  • (Optional) TF-IDF Supplemental
  • Practice lab: Non-Negative Matrix Factorization

3 Videos

  • Non Negative Matrix Factorization
  • Non Negative Matrix Factorization Notebook - Part 1
  • Non Negative Matrix Factorization Notebook - Part 2

Module Summary & Assessment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded: Module 6 Quiz

1 Readings

  • Summary

Course Final Project

    • 1 PeerReview
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1 Peer Review

  • Course Final Project

Auto Summary

Discover the world of Unsupervised Machine Learning with this engaging course designed for aspiring data scientists. Delve into clustering and dimension reduction algorithms, learning to extract insights from unlabeled datasets. Led by experienced instructors on Coursera, this professional-level course spans 1380 minutes and offers a hands-on approach to mastering unsupervised learning techniques. Ideal for those with a background in Python, Data Cleaning, and foundational math concepts, this course is available through a Starter subscription.

Mark J Grover
Instructors

Mark J Grover

Miguel Maldonado
Instructors

Miguel Maldonado

Joseph Santarcangelo
Instructors

Joseph Santarcangelo

Xintong Li
Instructors

Xintong Li