This course demystifies artificial intelligence and gives you a practical toolkit you can use right away. We’ll cover the essentials—how machines learn, the differences between machine learning, deep learning, and generative AI, and how data, models, and evaluation fit together. Through short labs, you’ll try popular tools, build simple classifiers and prompts, and learn the basics of deployment, monitoring, and responsible use without drowning in math.

By the end, you’ll know how to spot high-value AI opportunities, ask better questions of models, and collaborate with technical teams with confidence. We’ll explore real-world cases from marketing automation and customer support to fraud detection, forecasting, and document summarization, highlighting impact, risks, and guardrails. You’ll walk away with a portfolio-ready mini project and a clear roadmap for putting AI to work in your job or business.

I’m Guido Bonelli, a teacher who works at the intersection of education and artificial intelligence. My experience centers on helping people make sense of AI—translating technical ideas into clear, practical steps, and supporting learners as they apply them in studies, work, and everyday problem-solving.

In AI Essentials, I bring that approach to the classroom with a focus on fundamentals, responsible use, and hands-on practice. I’m deeply connected to this course because it reflects what I care about most: empowering learners to build confidence with AI, ask better questions, and use these tools thoughtfully to create real impact.