Coursera Project Network
Interpretable machine learning applications: Part 3
Coursera Project Network

Interpretable machine learning applications: Part 3

Taught in English

Included with Coursera Plus

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
4.3

(13 reviews)

What you'll learn

  • Import, explore and normalize real world data (HELOC) for evaluating the risk performance of mortgage applications

  • Train and test a prediction model as a Sequential model based Artificial Neural Network (ANN)

  • Generate explanations based on profiles of mortgage applicants closest to the individual requesting the explanation.

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
4.3

(13 reviews)

See how employees at top companies are mastering in-demand skills

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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. By the end of task 1, you will be able, as a data scientist or loan officer persona, to load, process and normalize the (HELOC) dataset about mortgage applications for training purposes.

  2. By the end of task 2, you will be able to define, train and evaluate an artificial neural network based classifier  by using TensorFlow.

  3. By the end of tasks 3 and 4, you will be able to obtain similar samples as explanations for a mortgage applicant predicted as "Good" and "Bad", respectively.

  4. By the end of task 5, you will be able to provide contrastive explanations for decisions affecting individual cases.

Recommended experience

Some introductory knowledge in machine learning and statistics. Some familiarization with Python programming environments.

4 project images

Instructor

Epaminondas Kapetanios
Coursera Project Network
5 Courses3,067 learners

Offered by

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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