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AI Product Management Specialization

Description

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. This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems. Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards. The courses in this Specialization focus on the intuition behind these technologies, with no programming required, and merge theory with practical information including best practices from industry.  Professionals and aspiring professionals from a diverse range of industries and functions, including product managers and product owners, engineering team leaders, executives, analysts and others will find this program valuable.  Applied Learning Project Learners will implement three projects throughout the course of this Specialization: 1) In Course 1, you will complete a hands-on project where you will create a machine learning model to solve a simple problem (no coding necessary) and assess your model's performance. 2) In Course 2, you will identify and frame a problem of interest, design a machine learning system which can help solve it, and begin the development of a project plan. 3) In Course 3, you will perform a basic user experience design exercise for your ML-based solution and analyze the relevant ethical and privacy considerations of the project. Read more

Microcredentials

Coursera

Free to Audit

4 months at 5 hours a week

Beginner

Paid Certificate

AI Product Management Specialization

Affiliate notice

  • Type
    Microcredentials
  • Provider
    Coursera
  • Pricing
    Free to Audit
  • Duration
    4 months at 5 hours a week
  • Difficulty
    Beginner
  • Certificate
    Paid Certificate

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. This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems. Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards. The courses in this Specialization focus on the intuition behind these technologies, with no programming required, and merge theory with practical information including best practices from industry.  Professionals and aspiring professionals from a diverse range of industries and functions, including product managers and product owners, engineering team leaders, executives, analysts and others will find this program valuable.  Applied Learning Project Learners will implement three projects throughout the course of this Specialization: 1) In Course 1, you will complete a hands-on project where you will create a machine learning model to solve a simple problem (no coding necessary) and assess your model's performance. 2) In Course 2, you will identify and frame a problem of interest, design a machine learning system which can help solve it, and begin the development of a project plan. 3) In Course 3, you will perform a basic user experience design exercise for your ML-based solution and analyze the relevant ethical and privacy considerations of the project. Read more