Clustering

Everyone
  • 13 videos | 1h 11m 9s
  • Includes Assessment
Rating 4.0 of 1 users Rating 4.0 of 1 users (1)
How do we get from raw data to improving the level of performance? The answer is found in this opening course, which introduces us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.

WHAT YOU WILL LEARN

  • Describe what unsupervised learning is and why is it challenging
    Identify unsupervised learning problems
    Describe what clustering is
    Identify when to use clustering
    Understand the set up for the k-means algorithm
    Define the k-means clustering problem and understand the k-means algorithm as a way to solve it
    Evaluate the output of the k-means algorithm
  • Understand what happens when we don't get a desired result from the k-means algorithm
    Understand what may cause someone to go beyond k-means clustering
    Describe different notions of similarity and clustering
    Know how to prepare data so that the k-means algorithm will produce the best results
    Understand how the number of clusters in data may not always be finite
    Know that clustering is not always the right answer for finding the patterns in data

IN THIS COURSE

  • 7m 8s
    Start off the course by learning what unsupervised learning is and understand what its challenges are. FREE ACCESS
  • 5m 55s
    Now you know what unsupervised learning is, let's learn how to identify these types of problems. FREE ACCESS
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    3.  What Is Clustering?
    5m 46s
    There are a lot of different problems that can be solved with unsupervised learning. Now learn about the most popular problem, clustering. FREE ACCESS
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    4.  When To Use Clustering
    6m 24s
    In the last video you learned that clustering is a particular form of unsupervised learning. Now go through more examples of clustering and see why and when you might want to use it in practice FREE ACCESS
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    5.  K-Means Preliminaries
    5m 24s
    Now that you have learned about clustering, learn about the most popular algorithm for clustering. FREE ACCESS
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    6.  The K-Means Algorithm
    6m 10s
    In the last video, we set up the k-means clustering problem as a particular subset of general clustering problems. Now develop the k-means algorithm. FREE ACCESS
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    7.  How To Evaluate Clustering
    6m 6s
    The last couple of videos have set up the k-means algorithm. Learn how to evaluate the output of this algorithm.  FREE ACCESS
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    8.  Beyond K-Means: What Really Makes A Cluster?
    3m 24s
    Now that you understand the algorithm, what happens if the output is unexpected or unwanted? Learn how to troubleshoot the k-means algorithm. FREE ACCESS
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    9.  Beyond K-Means: Other Notions Of Distance
    4m 58s
    Explore the notion of what makes a cluster and what motivates us to look at clustering problems and models beyond K-Means clustering. FREE ACCESS
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    10.  Beyond K-Means: Grouping Data By Similarity
    4m 36s
    Learn about different notions of similarity and clustering other than the squared Euclidean distance required. FREE ACCESS
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    11.  Beyond K-Means: Data And Pre-Processing
    5m 37s
    Take a closer look at data and how to prepare it for the k-means algorithm. FREE ACCESS
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    12.  Beyond K-Means: Big Data and Nonparametric Bayes
    4m 37s
    You have learned a lot about a fixed number of clusters. Learn about why that may not always be the case. FREE ACCESS
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    13.  Beyond Clustering
    5m 5s
    All of the videos before have talked about clustering. Now learn why that may not always be the best method. FREE ACCESS

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