Dimensionality Reduction & Spectral Techniques

  • 8 Videos | 40m 34s
  • Includes Assessment
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How do we get from raw data to improving the level of performance? The answer is found in this opening course. This course will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.

WHAT YOU WILL LEARN

  • Understand what networks are and how to analyze them
    Be able to use PCA
    Know how to use eigenvectors and the covariance matrix
    Understand how clustering occurs in graphs and networks
  • Know how eigenvectors can be used to capture the connectivity structure of a large network
    Know how to use the eigenvectors of the Laplacian matrix to find meaningful clusters that respect hidden structure in the data
    Understand modularity clustering and how it works
    Know what embeddings are and understand their uses

IN THIS COURSE

  • Playable
    1. 
    Networks and Complex Data
    4m 7s
    UP NEXT
  • Playable
    2. 
    Finding Principal Components In Data & Applications
    5m 11s
  • Locked
    3. 
    The Magic of Eigenvectors 1
    4m 50s
  • Locked
    4. 
    Clustering In Graphs And Networks
    4m 33s
  • Locked
    5. 
    The Magic of Eigenvectors 2
    5m 16s
  • Locked
    6. 
    Spectral Clustering
    5m 30s
  • Locked
    7. 
    Modularity Clustering
    5m 23s
  • Locked
    8. 
    Embeddings And Components
    5m 45s