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Machine Learning Foundations for Product Managers
  • Level Foundation
  • Duration 16 hours
  • Course by Duke University
  • Offered by

About

In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem. At the conclusion of this course, you should be able to: 1) Explain how machine learning works and the types of machine learning 2) Describe the challenges of modeling and strategies to overcome them 3) Identify the primary algorithms used for common ML tasks and their use cases 4) Explain deep learning and its strengths and challenges relative to other forms of machine learning 5) Implement best practices in evaluating and interpreting ML models

Modules

Course Overview

    • 3 Videos
    • 3 Readings
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3 Videos

  • Specialization Overview
  • Instructor Introduction
  • Course Overiew

3 Readings

  • About the Course
  • Important Reminder
  • Module 1 Slides

Introduction to Machine Learning

    • 6 Videos
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6 Videos

  • Module 1 Introduction & Objectives
  • Introduction to Machine Learning
  • Data Terminology
  • What is a Model?
  • Types of Machine Learning
  • What ML Can and Cannot Do

Review

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 1 Quiz

1 Videos

  • Module Wrap-up

Building a Model

    • 4 Videos
    • 1 Readings
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4 Videos

  • Introduction and Objectives
  • Building a Model
  • Feature Selection
  • Algorithm Selection

1 Readings

  • Download Module Slides

Model Selection

    • 3 Videos
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3 Videos

  • Bias-Variance Tradeoff
  • Test and Validation Sets
  • Cross Validation

Review

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 2 Quiz

1 Videos

  • Module Wrap-up

Metrics in ML Projects

    • 3 Videos
    • 1 Readings
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3 Videos

  • Introduction and Objectives
  • Outcomes vs Outputs
  • Model Output Metrics

1 Readings

  • Download Module Slides

Regression and Classification Metrics

    • 3 Videos
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3 Videos

  • Regression Error Metrics
  • Classification Error Metrics: Confusion Matrix
  • Classification Error Metrics: ROC and PR Curves

Review

    • 2 Videos
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Module 3 Quiz

1 Discussions

  • Outcomes & Output Metrics

2 Videos

  • Troubleshooting Model Performance
  • Module Wrap-up

Linear Regression & Regularization

    • 3 Videos
    • 1 Readings
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3 Videos

  • Introduction and Objectives
  • Linear Regression
  • Regularization

1 Readings

  • Download Module Slides

Logistic Regression

    • 2 Videos
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2 Videos

  • Logistic Regression
  • Softmax Regression

Review

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 4 Quiz

1 Videos

  • Module Wrap-up

Tree and Ensemble Models

    • 4 Videos
    • 1 Readings
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4 Videos

  • Introduction and Objectives
  • Tree Models
  • Ensemble Models
  • Random Forest

1 Readings

  • Download Module Slides

Clustering

    • 2 Videos
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2 Videos

  • Clustering
  • K-Means Clustering

Review

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 5 Quiz

1 Videos

  • Module Wrap-up

Neural Networks

    • 5 Videos
    • 1 Readings
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5 Videos

  • Introduction and Objectives
  • Introduction to Deep Learning
  • Artificial Neurons
  • From Neurons to Neural Networks
  • Training Neural Networks

1 Readings

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Applications of Deep Learning

    • 2 Videos
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2 Videos

  • Computer Vision
  • Natural Language Processing

Review

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Module 6 Quiz

1 Videos

  • Module Wrap-up

Course Wrap Up

    • 1 Videos
    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Course Project

1 Videos

  • Course Wrap-up

1 Readings

  • Course Project Modeling Options

Auto Summary

The "Machine Learning Foundations for Product Managers" course, offered by Duke University's Pratt School of Engineering on Coursera, provides a non-coding introduction to machine learning. Aimed at product managers and those collaborating with AI teams, the course covers ML fundamentals, model development, evaluation, and interpretation. It includes a hands-on project to train and optimize a ML model. With a duration of 960 minutes, the course is part of the AI Product Management Specialization and is available through a Starter subscription.

Jon Reifschneider
Instructor

Jon Reifschneider