Kernel Principal Component Analysis and Multidimensional Scaling

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Destrezas que aprenderás

Dimensionality Reduction, Unsupervised Learning, Cluster Analysis, K Means Clustering, Principal Component Analysis (PCA)

Reseñas

4.7 (160 calificaciones)

  • 5 stars
    79,37 %
  • 4 stars
    14,37 %
  • 3 stars
    2,50 %
  • 2 stars
    1,87 %
  • 1 star
    1,87 %

AF

6 de nov. de 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

MK

21 de feb. de 2022

Thank you Coursera.

Thank you IBM.

Thank you to all instructors.

De la lección

Nonlinear and Distance-Based Dimensionality Reduction

This module introduces dimensionality reduction techniques like Kernal Principal Component Analysis and multidimensional scaling. These methods are more powerful than Principal Component Analysis in many applications.

Impartido por:

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    Mark J Grover

    Digital Content Delivery Lead

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    Miguel Maldonado

    Machine Learning Curriculum Developer

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    Joseph Santarcangelo

    Ph.D., Data Scientist at IBM

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    Xintong Li

    Data Scientist at IBM

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