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Linear Regression with Python

Amit Yadav

Instructor: Amit Yadav

12,141 already enrolled

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

(424 reviews)

Intermediate level

Recommended experience

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

(424 reviews)

Intermediate level

Recommended experience

2 hours
Learn at your own pace
Hands-on learning

What you'll learn

  • Create a linear model, and implement gradient descent.

  • Train the linear model to fit given data using gradient descent.

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

  2. Dataset

  3. Initialize Parameters

  4. Forward Pass

  5. Compute Loss

  6. Backward Pass

  7. Update Parameters

  8. Training Loop

  9. Predictions

  10. Additional Example

Recommended experience

Some programming experience in Python is required. Understanding of the theory behind logistic regression, gradient descent is required.

7 project images

Instructor

Instructor ratings
4.6 (28 ratings)
Amit Yadav
Coursera Project Network
12 Courses172,834 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 424

4.6

424 reviews

  • 5 stars

    65.80%

  • 4 stars

    27.12%

  • 3 stars

    5.42%

  • 2 stars

    0.94%

  • 1 star

    0.70%

AR
4

Reviewed on Apr 2, 2020

PP
5

Reviewed on May 24, 2020

KM
4

Reviewed on May 14, 2020

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