Part two of Elements of AI: how the algorithms behind AI actually work, from optimisation and probability to machine learning and neural networks, with optional coding exercises in Python for those who want them. Free, from the same team as the introduction course already listed.
A plain-language introduction to what AI is, how it solves problems and how machine learning and neural networks work, with no programming or heavy maths. It closes by looking at what AI means for jobs and society. One of the most widely taken free AI courses in the world.
Objectives
Understand the main algorithms behind modern AI
Work through optimisation, probability and machine learning examples
Optionally implement simple methods in Python
Explain what AI is and is not, and how it relates to nearby fields
Describe how AI searches for solutions and handles uncertainty using probability
Tell the main types of machine learning apart and give examples
Outline how neural networks are built and what they are good at
Judge claims about AI in the news more critically
Discuss the likely social and workplace effects of AI
Things to know
Some maths; the intermediate track expects comfort with basic algebra
Provider update date not shown
Provider estimates 4 to 8 hours per part across 6 parts, so total time is about 24 to 48 hours
Content predates much of the generative AI wave, so it covers foundations rather than tools like ChatGPT
ECTS credits only available through the Open University of the University of Helsinki
Free account needed to track progress and get the certificate
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