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Support Vector Machines in Python, From Start to Finish
Coursera Project Network

Support Vector Machines in Python, From Start to Finish

Taught in English

Josh Starmer

Instructor: Josh Starmer

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Guided Project

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

Intermediate level

Recommended experience

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

(148 reviews)

What you'll learn

  • Import data into, and manipulating a pandas dataframe

  • Format the data for a support vector machine, including One-Hot Encoding and missing data.

  • Optimize parameters for the radial basis function and classification

  • Build, evaluate, draw and interpret a support vector machine

Details to know

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Guided Project

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

Intermediate level

Recommended experience

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

(148 reviews)

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies
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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. Import the modules that will do all the work

  2. Import the data

  3. Missing Data Part 1: Identifying Missing Data

  4. Missing Data Part 2: Dealing With Missing Data

  5. Format Data Part 1: Split the Data into Dependent and Independent Variables

  6. Format the Data Part 2: One-Hot Encoding

  7. Format the Data Part 3: Centering and Scaling

  8. Build A Preliminary Support Vector Machine

  9. Optimize Parameters with Cross Validation

  10. Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine

Recommended experience

Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.

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Instructor

Instructor ratings
4.5 (5 ratings)
Josh Starmer
Coursera Project Network
2 Courses12,450 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

148 reviews

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