Discover AI's potential across industries with this beginner-friendly course. Learn core concepts, applications, and ethical considerations.
Discover AI's potential across industries with this beginner-friendly course. Learn core concepts, applications, and ethical considerations.
This comprehensive introduction to AI explores fundamental concepts and practical applications for decision-makers. Students learn about AI's core components: data, algorithms, and interfaces. The course emphasizes a jargon-free approach to understanding AI technologies, their possibilities, and limitations. Designed for business leaders and professionals, it covers essential topics including different types of AI, real-world applications, and ethical implementation guidelines.
Instructors:
English
Deutsch, हिन्दी, Русский, 17 more
What you'll learn
Define AI using the three-part framework of data, algorithm, and interface
Identify and evaluate common AI technologies and applications
Understand the essential mechanics of how AI works
Learn to implement AI solutions while recognizing limitations
Develop strategic approaches to AI adoption in business
Master ethical considerations in AI implementation
Skills you'll gain
This course includes:
24 Minutes PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This comprehensive course introduces decision-makers to the fundamentals and applications of artificial intelligence. Through a single focused module, students explore AI concepts, technologies, and implementations with minimal technical jargon. The curriculum covers essential topics including AI definitions, types of AI, operational mechanics, and practical applications. Special emphasis is placed on strategic understanding and ethical implementation of AI in professional settings.
Exploring AI Possibilities
Module 1 · 5 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Bridging Data Science, Ecology, and Genomics Research
Ava Hoffman, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center, where she leads groundbreaking initiatives in making genomics research more accessible through cloud-based resources. Her unique expertise spans both ecological and data sciences, with particular focus on population genetics and statistical modeling in natural environments. As an instructor for multiple courses including "AI for Decision Makers," "AI for Efficient Programming," "Data Management and Sharing for NIH Proposals," and "Exploring AI Possibilities," she brings a practical approach to complex topics. Her research interests include urban plant evolution and its connection to public health outcomes, particularly studying how plants adapt to city environments. Beyond her academic work, she serves on the board of Slow Food Baltimore, demonstrating her commitment to sustainable practices. Her work with the AnVIL Project and the Genomic Data Science Community Network (GDSCN) showcases her dedication to democratizing genomics research. She combines her technical expertise with practical applications, engaging in activities from coding to climbing, while maintaining a DIY approach to problem-solving.
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Frequently asked questions
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