Overview

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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.

Syllabus

Getting Started with Machine Learning

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.

Decision Criteria for Machine Learning Implementation

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.

Managing Machine Learning Projects

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.

Data Acquisition and Preparation for Machine Learning

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.

Preprocessing Techniques for Machine Learning

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.

Algorithm Selection and ML Solution Development

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.

Model Evaluation Metrics and Performance Optimization

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.

ML Model Deployment and Monitoring

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.