Clustering and Unsupervised Learning
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Clustering and Unsupervised Learning
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 36M | 163 MB
Genre: eLearning | Language: English
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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