Dimensionality Reduction using an Autoencoder in Python
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Dimensionality Reduction using an Autoencoder in Python

Highlights

In this 1-hour long project, you will learn how to generate your own high-dimensional dummy dataset. You will then learn how to preprocess it effectively before training a baseline PCA model. You will learn the theory behind the autoencoder, and how to train one in scikit-learn. You will also learn how to extract the encoder portion of it to reduce dimensionality of your input data. In the course of this project, you will also be exposed to some basic clustering strength metrics. Note: This course works best for learners who are based in the North America region. We're currently working on providing the same experience in other regions.

About the Course Provider

Coursera provides access to more than 3000+ courses across a wide variety of subjects in parntership with different universities and organizations.

Course by

  • self
    Self paced
  • dueration
    Duration 2 hours
  • domain
    Domain Data Science & AI
  • subs
    Monthly Subscription Option not available
  • fee
    Buy Now Free
  • language
    Language English