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Principal Component Analysis with NumPy

Snehan Kekre

Instructor: Snehan Kekre

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Learn, practice, and apply job-ready skills with expert guidance
4.6

(293 reviews)

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning
Learn, practice, and apply job-ready skills with expert guidance
4.6

(293 reviews)

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning

What you'll learn

  • Implement Principal Component Analysis (PCA) from scratch with NumPy and Python

  • Conduct basic exploratory data analysis (EDA)

  • Create simple data visualizations with Seaborn and Matplotlib

Details to know

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Taught in English
No downloads or installation required

Only available on desktop

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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. Introduction and Overview

  2. Load the Data and Libraries

  3. Visualize the Data

  4. Data Standardization

  5. Compute the Eigenvectors and Eigenvalues

  6. Singular Value Decomposition (SVD)

  7. Selecting Principal Components Using the Explained Variance

  8. Project Data Onto a Lower-Dimensional Linear Subspace

Recommended experience

Prior programming experience in Python and machine learning theory is recommended.

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Instructor

Instructor ratings
4.5 (14 ratings)
Snehan Kekre
Coursera Project Network
11 Courses108,961 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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Learner reviews

Showing 3 of 293

4.6

293 reviews

  • 5 stars

    71.67%

  • 4 stars

    22.18%

  • 3 stars

    3.75%

  • 2 stars

    0.68%

  • 1 star

    1.70%

PP
4

Reviewed on May 31, 2020

AA
4

Reviewed on Aug 4, 2020

TA
5

Reviewed on Oct 30, 2020

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