This digital course is designed to help business decision makers understand the fundamentals of machine learning (ML). • Course level: Fundamental • Duration: 30 minutes Activities: This course includes presentations, videos, and knowledge assessments. Course objectives: In this course, you will learn to: •Understand the basics of machine learning to help evaluate the benefits and risks associated with adopting ML in various business cases Intended audience: This course is intended for: •Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects •Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshops Prerequisites: We recommend that attendees of this course have: •Basic knowledge of computers and computer systems •Some basic knowledge of the concept of machine learning Course outline: Module 1: How can machine learning help? •Define artificial intelligence •Define machine learning •Describe the different business domains impacted by machine learning •Describe the positive feedback loop (flywheel) that drives ML projects • Describe the potential for machine learning in underutilized markets Module 2: How does machine learning work? •Describe artificial intelligence •Describe the difference between artificial intelligence and machine learning Module 3: What are some potential problems with machine learning? •Describe the differences between simple and complex models •Understand unexplainability and uncertainty problems with machine learning models Module 4: Conclusion
