O’Reilly – Clustering and Unsupervised Learning

O’Reilly – Clustering and Unsupervised Learning
English | Size: 163.25 MB
Category: Tutorioal

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

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