Packt

Machine Learning Product Management - Strategy to Deployment

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Packt

Machine Learning Product Management - Strategy to Deployment

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the essential responsibilities and skills of a Machine Learning Product Manager.

  • Learn how to evaluate when machine learning is the right tool for a product.

  • Develop the ability to structure ML teams and manage complex ML projects.

  • Gain practical knowledge in selecting the right algorithms and deploying ML models into production.

Details to know

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Recently updated!

April 2026

Assessments

9 assignments

Taught in English

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There are 8 modules in this course

In this module, we will introduce you to the core concepts of machine learning product management, the essential role of an ML Product Manager, and key terminology. You'll also learn how machine learning is transforming industries and how to identify products for ML integration.

What's included

13 videos1 reading

In this module, we will guide you through the decision-making process for implementing machine learning in your product. You’ll learn how to evaluate when ML is the best solution, the data needs for a project, and the common challenges to watch out for.

What's included

7 videos1 assignment

In this module, we will explore the unique role of an ML Product Manager, how to structure teams for success, and what each phase of the ML project lifecycle entails. You'll also get hands-on experience with formulating and validating project hypotheses.

What's included

7 videos1 assignment

In this module, we will cover strategies for acquiring and preparing data for machine learning models. You will explore data acquisition techniques, data storage options, and methods to structure data for successful ML model training.

What's included

8 videos1 assignment

In this module, we will guide you through essential preprocessing techniques including data cleaning, transformation, and feature engineering. You’ll also learn how to split and sample data effectively for your ML models.

What's included

5 videos1 assignment

In this module, we will dive into selecting the right machine learning algorithm for your project, exploring various types of models including regression, classification, and anomaly detection. You’ll also learn when to build, buy, or outsource solutions.

What's included

8 videos1 assignment

In this module, we will explore how to evaluate and optimize machine learning models using metrics like the confusion matrix, precision, and recall. You’ll also learn strategies for continuous performance improvement and user experience optimization.

What's included

5 videos1 assignment

In this module, we will take you through the steps of deploying your machine learning model into production and discuss how to monitor its performance. You will also learn strategies for keeping your model optimized and scalable.

What's included

3 videos3 assignments

Instructor

Packt - Course Instructors
Packt
1,626 Courses472,392 learners

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Packt

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