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Logistic Regression with NumPy and Python

Snehan Kekre

Instructor: Snehan Kekre

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Guided Project
Learn, practice, and apply job-ready skills with expert guidance
4.5

(390 reviews)

Beginner level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning
Guided Project
Learn, practice, and apply job-ready skills with expert guidance
4.5

(390 reviews)

Beginner level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning

What you'll learn

  • Implement the gradient descent algorithm from scratch

  • Perform logistic regression with NumPy and Python

  • Create data visualizations with Matplotlib and Seaborn

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 Project Overview

  2. Load the Data and Import Libraries

  3. Visualize the Data

  4. Define the Logistic Sigmoid Function 𝜎(𝑧)

  5. Compute the Cost Function 𝐽(𝜃) and Gradient

  6. Cost and Gradient at Initialization

  7. Implement Gradient Descent

  8. Plotting the Convergence of 𝐽(𝜃)

  9. Plotting the Decision Boundary

  10. Predictions Using the Optimized 𝜃 Values

Recommended experience

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

7 project images

Instructor

Instructor ratings
4.5 (15 ratings)
Snehan Kekre
Coursera Project Network
11 Courses107,487 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 390

4.5

390 reviews

  • 5 stars

    64.87%

  • 4 stars

    27.17%

  • 3 stars

    5.12%

  • 2 stars

    0.76%

  • 1 star

    2.05%

PP
5

Reviewed on Apr 3, 2020

AS
5

Reviewed on Aug 29, 2020

AS
4

Reviewed on Jul 14, 2020

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