Duke University
Machine Learning Foundations for Product Managers
Duke University

Machine Learning Foundations for Product Managers

This course is part of AI Product Management Specialization

Taught in English

Some content may not be translated

Jon Reifschneider

Instructor: Jon Reifschneider

35,586 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(364 reviews)

|

93%

Beginner level

Recommended experience

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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(364 reviews)

|

93%

Beginner level

Recommended experience

15 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
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There are 6 modules in this course

In this module we will be introduced to what machine learning is and does. We will build the necessary vocabulary for working with data and models and develop an understanding of the different types of machine learning. We will conclude with a critical discussion of what machine learning can do well and cannot (or should not) do.

What's included

10 videos2 readings1 quiz1 discussion prompt

In this module we will discuss the key steps in the process of building machine learning models. We will learn about the sources of model complexity and how complexity impacts a model's performance. We will wrap up with a discussion of strategies for comparing different models to select the optimal model for production.

What's included

8 videos1 reading1 quiz1 discussion prompt

In this module we will learn how to define appropriate outcome and output metrics for AI projects. We will then discuss key metrics for evaluating regression and classification models and how to select one for use. We will wrap up with a discussion of common sources of error in machine learning projects and how to troubleshoot poor performance.

What's included

8 videos1 reading1 quiz1 discussion prompt

In this module we will explore the use of linear models for regression and classification. We will begin with introducing linear regression and continue with a discussion on how to make linear regression work better through regularization. We will then switch to classification and introduce the logistic regression model for both binary and multi-class classification problems.

What's included

6 videos1 reading1 quiz

We will begin this model with a discussion of tree models and their value in modeling compex non-linear problems. We will then introduce the method of creating ensemble models and their benefits. We will wrap this module up by switching gears to unsupervised learning and discussing clustering and the popular K-Means clustering approach.

What's included

7 videos1 reading1 quiz1 discussion prompt

Our final module in this course will focus on a hot area of machine learning called deep learning, or the use of multi-layer neural networks. We will develop an understanding of the intuition and key mathematical principles behind how neural networks work. We will then discuss common applications of deep learning in computer vision and natural language processing. We will wrap up the course with our course project, where you will have an opportunity to apply the modeling process and best practices you have learned to create your own machine learning model.

What's included

9 videos2 readings1 quiz1 peer review1 discussion prompt

Instructor

Instructor ratings
4.6 (145 ratings)
Jon Reifschneider
Duke University
3 Courses41,969 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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4.6

364 reviews

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KV
5

Reviewed on Jun 23, 2023

JP
5

Reviewed on Apr 24, 2022

JE
4

Reviewed on Dec 16, 2023

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