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Starts 8 June 2025 02:02

Ends 8 June 2025

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Introduction to Machine Learning: Art of the Possible

This digital course is designed to help business decision makers grasp the essentials of machine learning (ML), aimed at empowering them with the knowledge to assess its advantages and risks within different business scenarios. Course level: Fundamental Duration: 30 minutes The course curriculum encompasses presentations, videos, and interactive kn.
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Overview

This digital course is designed to help business decision makers grasp the essentials of machine learning (ML), aimed at empowering them with the knowledge to assess its advantages and risks within different business scenarios.

Course level:

Fundamental

Duration:

30 minutes

The course curriculum encompasses presentations, videos, and interactive knowledge assessments, ensuring a comprehensive learning experience.

Course Objectives:


Participants will:

  • Understand the basics of machine learning to assist in evaluating its benefits and risks in various business applications.

Intended Audience:


This course is tailored for:

  • Nontechnical business leaders and other decision makers involved or soon to be involved in ML projects.
  • Attendees of the AWS Machine Learning Embark program and participants in Machine Learning Solutions Lab (MLSL) discovery workshops.

Prerequisites:


Prospective attendees are recommended to have:

  • Basic familiarity with computers and computer systems.
  • An introductory understanding of machine learning concepts.

Course Outline:

  • Module 1:

    How Can Machine Learning Help?

    • Define artificial intelligence and machine learning.
    • Discuss the impact of machine learning across various business domains.
    • Explain the concept of the positive feedback loop (flywheel) in ML projects.
    • Identify opportunities for machine learning in underutilized markets.
  • Module 2:

    How Does Machine Learning Work?

    • Delineate between artificial intelligence and machine learning.
  • Module 3:

    Potential Problems with Machine Learning

    • Differentiate between simple and complex models.
    • Address issues related to unexplainability and uncertainty in machine learning models.
  • Module 4:

    Conclusion

Provider:

AWS Skill Builder

Categories:

Machine Learning Courses


Subjects

united states