This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
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课程信息
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第 2 门课程（共 3 门）
初级
No prior experience in machine learning or programming required.
完成时间大约为18 小时
英语（English）
字幕：英语（English）
您将获得的技能
- Modeling
- Project Management
- Machine Learning
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
初级
No prior experience in machine learning or programming required.
完成时间大约为18 小时
英语（English）
字幕：英语（English）
提供方
杜克大学
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
授课大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Identifying Opportunities for Machine Learning
In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.
完成时间为 3 小时
9 个视频 （总计 42 分钟）, 3 个阅读材料, 1 个测验
完成时间为 3 小时
Organizing ML Projects
In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will also walk through the key roles on a ML project team and how to organize work.
完成时间为 3 小时
8 个视频 （总计 65 分钟）, 2 个阅读材料, 1 个测验
完成时间为 3 小时
Data Considerations
In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerations in sourcing data, cleaning data, and developing and selecting a feature set to use in modeling. The module will conclude with a discussion on best practices to ensure reproducibility of your data pipeline.
完成时间为 3 小时
8 个视频 （总计 58 分钟）, 2 个阅读材料, 1 个测验
完成时间为 3 小时
ML System Design & Technology Selection
In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologies used to build ML models.
完成时间为 3 小时
8 个视频 （总计 51 分钟）, 2 个阅读材料, 1 个测验
关于 AI Product Management 专项课程
Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects.
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