SAS
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
SAS

Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

This course is part of Machine Learning Rock Star – the End-to-End Practice Specialization

Taught in English

Eric Siegel

Instructor: Eric Siegel

4,602 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.8

(76 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and more

  • Plan ML: Determine the way machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there

  • Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues

  • Lead ML: Manage a machine learning project, from the generation of predictive models to their launch

Details to know

Shareable certificate

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Assessments

51 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(76 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

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

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Build your subject-matter expertise

This course is part of the Machine Learning Rock Star – the End-to-End Practice Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

This module dives deeply into the business applications of machine learning – for marketing, financial services, fraud detection and more. We'll illustrate the value delivered for these domains by way of case studies and detailed examples. And we'll precisely measure the performance of the predictive models themselves, focusing on model lift, a predictive multiplier that tells you the improvement achieved by a model.

What's included

13 videos5 readings14 quizzes1 peer review2 discussion prompts2 plugins

To make machine learning work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This module will demonstrate that practice, guiding you to lead the end-to-end implementation of machine learning.

What's included

12 videos7 readings12 quizzes1 peer review2 discussion prompts

The greatest technical hands-on bottleneck of a machine learning project is the preparation of the training data – which is the raw material that predictive modeling software crunches, munches, and learns from. This module will guide you to prepare that data. Business priorities are front and center in the process, since they directly inform the data requirements, including the specific meaning of the dependent variable, which is the outcome or behavior your model will actually predict.

What's included

14 videos2 readings15 quizzes1 peer review2 discussion prompts

For many machine learning projects, high accuracy is unattainable – and, besides, accuracy isn't the right metric in the first place. The first portion of this module will demonstrate how other metrics, such as the costs incurred by prediction errors, better serve to keep a machine learning project on track. Then we'll turn to the social good that can be achieved with machine learning, and we'll cover more social justice risks, including the hazards of predicting sensitive information such as pregnancy, job resignations, death, and ethnicity. We'll wrap up by examining the promise and perils of predictive policing.

What's included

9 videos4 readings10 quizzes3 discussion prompts

Instructor

Instructor ratings
4.8 (17 ratings)
Eric Siegel
SAS
5 Courses15,934 learners

Offered by

SAS

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4.8

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