Duke University

Managing Machine Learning Projects

This course is part of AI Product Management Specialization

Taught in English

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Jon Reifschneider

Instructor: Jon Reifschneider

11,514 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(123 reviews)

Beginner level

Recommended experience

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

Details to know

Shareable certificate

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(123 reviews)

Beginner level

Recommended experience

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

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

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This course is part of the AI Product Management 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
  • Earn a shareable career certificate
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There are 5 modules in this course

In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.

What's included

9 videos3 readings1 quiz3 discussion prompts

In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will also walk through the key roles on a ML project team and how to organize work.

What's included

8 videos2 readings1 quiz1 discussion prompt

In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerations in sourcing data, cleaning data, and developing and selecting a feature set to use in modeling. The module will conclude with a discussion on best practices to ensure reproducibility of your data pipeline.

What's included

8 videos2 readings1 quiz1 discussion prompt

In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologies used to build ML models.

What's included

8 videos2 readings1 quiz1 discussion prompt

The final module in the course focuses on identifying and mitigating the key issues which ML models experience once they are in production. We will discuss how to set up a robust ML system monitoring capability and define a model maintenance plan to maintain high performance of a production model. We will conclude with a discussion on the importance of versioning in ML systems to facilitate continued rapid iteration even after deployment.

What's included

8 videos2 readings1 quiz1 peer review1 discussion prompt1 plugin

Instructor

Instructor ratings
4.8 (29 ratings)
Jon Reifschneider
Duke University
3 Courses40,621 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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4.8

123 reviews

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MR
5

Reviewed on Dec 29, 2022

LR
5

Reviewed on Jun 29, 2023

LD
5

Reviewed on May 12, 2022

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