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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Programa de cursos integrados AI Product ManagementUniversidade DukeInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 3 no
Nível iniciante
No prior experience in machine learning or programming required.
Aprox. 18 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
- Modeling
- Project Management
- Machine Learning
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 3 no
Nível iniciante
No prior experience in machine learning or programming required.
Aprox. 18 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade Duke
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.
Programa - O que você aprenderá com este curso
3 horas para concluir
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.
3 horas para concluir
9 vídeos (Total 42 mín.), 3 leituras, 1 teste
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 65 mín.), 2 leituras, 1 teste
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 58 mín.), 2 leituras, 1 teste
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 51 mín.), 2 leituras, 1 teste
Sobre Programa de cursos integrados AI Product Management
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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