Duke University
Machine Learning Foundations for Product Managers
Duke University

Machine Learning Foundations for Product Managers

Jon Reifschneider

Instructor: Jon Reifschneider

45,210 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.6

(462 reviews)

Beginner level

Recommended experience

Flexible schedule
Approx. 14 hours
Learn at your own pace
92%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.6

(462 reviews)

Beginner level

Recommended experience

Flexible schedule
Approx. 14 hours
Learn at your own pace
92%
Most learners liked this course

Details to know

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Assessments

6 assignments

Taught in English

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This course is part of the AI Product Management Specialization
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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 videos3 readings1 assignment

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 assignment

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 assignment1 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 assignment

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 assignment

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 assignment1 peer review

Instructor

Instructor ratings
4.7 (178 ratings)
Jon Reifschneider
Duke University
3 Courses54,156 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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4.6

462 reviews

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Reviewed on Oct 18, 2023

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