Machine Learning Product Management - Strategy to Deployment
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Machine Learning Product Management - Strategy to Deployment
Included with
Ask Coursera
Recommended experience
Recommended experience
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.
Skills you'll gain
- Data Processing
- Machine Learning Algorithms
- Decision Intelligence
- Data Transformation
- Product Lifecycle Management
- Model Evaluation
- Model Optimization
- Machine Learning Methods
- Machine Learning
- MLOps (Machine Learning Operations)
- Feature Engineering
- Model Training
- Technical Product Management
- Project Management
- Data Preprocessing
- Product Management
- Technical Management
- AI Product Strategy
- Applied Machine Learning
Tools you'll learn
Details to know
April 2026
9 assignments
See how employees at top companies are mastering in-demand skills
There are 8 modules in this course
This course features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive deep into machine learning product management, gaining hands-on knowledge and insights into how machine learning is integrated into products. The course explores critical roles, skills, and real-world applications of ML, offering practical exercises that reinforce concepts and strategies. Through detailed lessons, you'll explore the lifecycle of an ML product, from ideation and team structuring to deployment and monitoring. You'll also learn to make strategic decisions on when machine learning is the right tool and how to avoid common pitfalls. The journey includes a detailed exploration of data acquisition, preparation, preprocessing, and algorithm selection, helping you gain a comprehensive understanding of the full machine learning lifecycle. With an emphasis on practical applications, you'll also have the opportunity to implement various ML strategies in real-world scenarios. This course is designed for aspiring machine learning product managers, data-driven professionals, and those interested in understanding the intersection of product management and machine learning. It does not require prior technical experience but a passion for the field is essential. By the end of the course, you will be able to evaluate data needs for ML, structure ML teams, choose suitable algorithms, and deploy models into production, among other key competencies.
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
13 videos•Total 61 minutes
- Course Overview•2 minutes
- Understanding the Role of an ML Product Manager•2 minutes
- Defining Machine Learning and Its Core Concepts•4 minutes
- Get to Know Your Instructor•2 minutes
- The Rise of Machine Learning in Industry•8 minutes
- Exercise #1: Identify Your Product for ML Integration•4 minutes
- How Machine Learning Algorithms Learn•6 minutes
- Supervised, Unsupervised, and Reinforcement Learning•8 minutes
- Exercise #2: Classify the Type of ML•5 minutes
- Introduction to Deep Learning and Neural Networks•7 minutes
- Real-World Applications of Machine Learning•4 minutes
- Key Terminology Every ML Product Manager Should Know•3 minutes
- Exercise #3: Apply Machine Learning Terminology in Context•6 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
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
7 videos•Total 36 minutes
- Understanding the AI Flywheel•4 minutes
- Common Pitfalls in ML Product Development•6 minutes
- When Machine Learning Is the Right Tool•4 minutes
- When Machine Learning Is Not the Answer•3 minutes
- Exercise #4: Do You Need Interpretability in Your Model?•6 minutes
- Evaluating Data Requirements for ML Implementation•3 minutes
- Exercise #5: Making the Call: ML or Not?•11 minutes
1 assignment•Total 15 minutes
- Decision Criteria for Machine Learning Implementation - Assessment•15 minutes
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
7 videos•Total 44 minutes
- The Unique Role of an ML Product Manager•3 minutes
- Structuring an Effective ML Team•5 minutes
- Core Roles in a Machine Learning Project•7 minutes
- Understanding the ML Project Lifecycle•14 minutes
- Exercise #6: Develop and Validate Your Hypothesis•8 minutes
- Exercise #7: Frame Your Machine Learning Challenge•3 minutes
- Exercise #8: Define the ML Problem Statement•4 minutes
1 assignment•Total 15 minutes
- Managing Machine Learning Projects - Assessment•15 minutes
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
8 videos•Total 37 minutes
- Data Acquisition Techniques for Machine Learning•10 minutes
- Leveraging Google reCAPTCHA for Data Collection•4 minutes
- Exercise #9: Identify User-Generated Data Labelling•3 minutes
- Problem Simplification in ML Data Design•2 minutes
- Exercise #10: Structuring Data for Model Input•3 minutes
- Top Open Datasets for Machine Learning Projects•5 minutes
- Estimating Data Requirements for ML Models•4 minutes
- Data Storage Options: Warehouse, Lake, and Graph•7 minutes
1 assignment•Total 15 minutes
- Data Acquisition and Preparation for Machine Learning - Assessment•15 minutes
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
5 videos•Total 21 minutes
- Data Cleaning and Scrubbing Techniques•6 minutes
- How to Sample and Split Data for ML Models•5 minutes
- Data Transformation Methods for Machine Learning•5 minutes
- Introduction to Feature Engineering Techniques•2 minutes
- Exercise #11: Brainstorming a New Feature for Your Model•3 minutes
1 assignment•Total 15 minutes
- Preprocessing Techniques for Machine Learning - Assessment•15 minutes
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
8 videos•Total 41 minutes
- How to Choose the Right Machine Learning Algorithm•2 minutes
- Build vs Buy vs Outsource: ML Solution Strategy•4 minutes
- Exploring Machine Learning as a Service (MLaaS)•7 minutes
- Regression Algorithms Explained: Linear, Polynomial, Logistic•5 minutes
- Classification Algorithms: SVM, K-NN, Decision Trees•9 minutes
- Clustering Algorithms: K-Means and Mean Shift•6 minutes
- Anomaly Detection with LOF and DBSCAN•5 minutes
- Ensemble Methods: Bagging, Boosting, and Stacking•4 minutes
1 assignment•Total 15 minutes
- Algorithm Selection and ML Solution Development - Assessment•15 minutes
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
5 videos•Total 28 minutes
- Understanding the Confusion Matrix•4 minutes
- Precision, Recall, and F1 Score Explained•7 minutes
- Exercise #12: Let's Calculate Evaluation Metrics•6 minutes
- Optimizing User Experience•5 minutes
- Exercise #13: Choosing the Right Metric•6 minutes
1 assignment•Total 15 minutes
- Model Evaluation Metrics and Performance Optimization - Assessment•15 minutes
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
3 videos•Total 15 minutes
- Deploying Your Machine Learning Model•6 minutes
- Monitoring Model Performance•9 minutes
- Course Summary and Next Steps•1 minute
3 assignments•Total 90 minutes
- ML Model Deployment and Monitoring - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
Instructor
Explore more from Software Development
- Status: Free TrialD
Duke University
Course
- Status: Preview
Course
- Status: Free TrialD
Duke University
Course
Course
Why people choose Coursera for their career
Frequently asked questions
Machine Learning Product Management is the process of managing the lifecycle of machine learning products from strategy to deployment. This includes understanding the role of a product manager, implementing machine learning solutions, and working closely with data scientists and engineers to bring innovative machine learning applications to market. It is highly relevant in today's data-driven world, as businesses increasingly leverage machine learning to create smarter products and optimize operations across industries.
This course is about mastering the fundamentals of machine learning product management. It covers key concepts of machine learning, including types of learning algorithms, the importance of data, the process of building a machine learning product, and how to deploy and monitor models. The course also explores how to evaluate when machine learning is the right tool and addresses common pitfalls in machine learning product development.
After completing this course, you will be able to understand and apply machine learning principles in product management. You'll know how to structure an effective machine learning team, evaluate data for ML models, choose appropriate algorithms, and oversee the deployment of machine learning models. Additionally, you will be equipped to make decisions on when to integrate machine learning into products and how to manage the end-to-end process of ML product development.
More questions
Financial aid available,