Zahra Quettawala
Program Overview
The Product Manager Machine Learning Program is designed to equip Capital One Product Managers with the skills needed to define, reframe, and solve complex problems using machine learning (ML). Over the 10-week program, participants will engage in hands-on learning with data, algorithms, and ML models, applying theoretical concepts directly to real-world scenarios.
Learning Outcomes
Explain the process of developing a model in detail: feature selection, model selection, model training, deploying, monitoring, and refitting.
Identify and articulate the experience and challenges a machine learning engineer faces when developing a model.
Demonstrate general awareness of the fundamental mathematical concepts behind common machine-learning approaches.
Compare different modeling approaches in practice and identify common pitfalls/limitations.
Effectively apply theoretical concepts covered in the course to real-world scenarios (using Python in an internal sandbox environment).
Describe the impact or role of the broader ecosystem in the model build and its ongoing use (e.g. understanding process integration; understanding risk & governance role in the model build and ongoing use).
Consider the ethical implications when building or leveraging ML solutions.
Identify ML opportunities, determine the business value, and articulate when ML should not be used.