AI Product Management is designed to empower professionals across diverse functions to understand the application of artificial intelligence (AI) and machine learning in product development. This Specialization by Duke University focuses on providing a comprehensive understanding of when and how AI can be applied to solve problems, as well as the human-centered design practices required to create AI products that protect privacy and meet ethical standards.
Learners will acquire skills in managing machine learning projects, including the data science process, ML model evaluation, and interpretation. The course also emphasizes the importance of leading ML projects using industry best practices and understanding the challenges and strategies associated with modeling. Participants will learn to identify opportunities for applying ML to solve user problems, and gain insights into the key technology decisions involved in ML system design.
The Specialization culminates in equipping learners with the ability to identify and mitigate privacy and ethical risks in AI projects, apply human-centered design practices to design successful AI product experiences, and build AI systems that inspire trust in users.
Certificate Available ✔
Get Started / More Info
The course consists of three modules: Machine Learning Foundations for Product Managers, Managing Machine Learning Projects, and Human Factors in AI. These modules cover essential topics such as understanding machine learning, managing ML projects, and addressing privacy and ethical considerations in AI.
In the first module, Machine Learning Foundations for Product Managers, participants will gain a foundational understanding of machine learning technology without the need for coding. This includes learning about common ML and deep learning algorithms, as well as best practices in evaluating and interpreting ML models.
The second module, Managing Machine Learning Projects, focuses on the practical aspects of managing ML projects, including identifying opportunities for ML, applying the data science process, and making key technology decisions in ML system design. Participants will also learn to lead ML projects using best practices.
The third module, Human Factors in AI, emphasizes the identification and mitigation of privacy and ethical risks in AI projects. Participants will also learn to apply human-centered design practices to create successful AI product experiences and build AI systems that inspire trust in users.
TensorFlow: Data and Deployment is a four-course Specialization that equips you to deploy machine learning models on various devices and platforms, utilizing TensorFlow.js,...
Learn to build a logistic regression model using Pyspark MLLIB to classify patients as diabetic or non-diabetic in this project-based course.
Explore ML pipelines on Google Cloud, including TFX, Kubeflow, and MLflow. Learn to orchestrate, automate, and manage ML pipelines across various frameworks.
Supervised Machine Learning: Regression equips aspiring data scientists with hands-on experience in training regression models to predict continuous outcomes and...