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Clustering and Unsupervised Learning

Video Description

This course introduces clustering, a common technique used widely in unsupervised machine learning. The course begins by defining what clustering means through graphical explanations, and describes the common applications of clustering. Next, it explores k-means clustering in detail, including the concepts of distance functions and k-modes; illustrates hierarchical clustering through visual examples of dendrograms, and discusses different types of clustering algorithms. The course ends with a comparison of the performance of different algorithms. An understanding of basic algebra is required and some knowledge of linear algebra will be helpful.

  • Understand what clustering is and learn how to perform k-means clustering
  • Explore key clustering concepts such as objective function, distance functions, and k-modes
  • Discover how hierarchical clustering works
  • Learn techniques like distribution-based clustering and density-based clustering
  • Understand the limitations of clustering and unsupervised learning
  • Learn how to use — and enjoy free access to — the SherlockML data science platform
  • Develop the skills required for the machine learning job market, where demand outstrips supply

Angie Ma, Gary Willis, and Alessandra Stagliano are data scientists with ASI Data Science, a London based AI/machine learning solutions firm. Angie co-founded ASI and is also the founder of Data Science Lab London, one of the biggest communities of data scientists and data engineers in Europe, with over 2,500 members. Angie holds a PhD in physics from London's University College, Gary Willis holds a PhD in statistical physics from London's Imperial College, and Alessandra Stagliano holds a PhD in computer science from the University of Genoa. Collectively, the group has worked on over 150 commercial AI/machine learning projects.

Table of Contents

  1. Clustering
    1. Introduction 00:03:51
    2. Overview 00:01:26
    3. Example datasets 00:04:52
    4. Applications 00:01:11
    5. K-means clustering 00:10:05
    6. Hierarchical clustering 00:04:59
    7. Other clustering algorithms 00:08:11
    8. Conclusion 00:02:11