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Dimensionality Reduction using an Autoencoder in Python
Coursera Project Network

Dimensionality Reduction using an Autoencoder in Python

Taught in English

Ari Anastassiou

Instructor: Ari Anastassiou

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

60 minutes
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.6

(99 reviews)

What you'll learn

  • How to generate and preprocess high-dimensional data

  • How an autoencoder works, and how to train one in scikit-learn

  • How to extract the encoder portion from a trained model, and reduce dimensionality of your input data

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

60 minutes
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.6

(99 reviews)

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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. An introduction to the problem and a summary of needed imports

  2. Dataset creation and preprocessing

  3. Using PCA as a baseline for model performance

  4. Theory behind the autoencoder architecture and how to train a model in scikit-learn

  5. Reducing dimensionality using the encoder half of an autoencoder within scikit-learn

Recommended experience

Intermediate Python users with some exposure to neural networks.

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Instructor

Instructor ratings
4.7 (9 ratings)
Ari Anastassiou
Coursera Project Network
10 Courses33,993 learners

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How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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4.6

99 reviews

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