The democratisation of AI education aims to make AI and machine learning education accessible, affordable, and diverse to all. Open-source initiatives are fostering democracies of AI education through online courses and platforms offering free or low-cost training. The increasing demand for AI skills is largely due to its interdisciplinary nature, with applications in healthcare, financial institutions, and agriculture. Open-source AI tools and resources, such as machine learning libraries like TensorFlow and PyTorch, are being used to disseminate AI knowledge and make education more accessible, flexible, and personalised. Massive Open Online Courses (MOOCs) platforms like Coursera, edX, and Fast.ai offer AI and ML courses, while adaptive learning platforms like Squirrel AI and DreamBox Learning provide personalised learning materials adapted in real time. Open-source AI education resources, such as Jupyter Notebook, Scikit-learn, and OpenAI Gym, offer interactive computing, ML algorithms, and specialised educational programmes for underrepresented groups. AI4ALL, a non-profit, youth-initiated software, provides access to AI education and career opportunities for underrepresented groups. Challenges include accessibility, particularly in underserved regions, and the need to address the digital divide. As AI becomes increasingly integral to various industries, the demand for AI skills will soar, motivating the expansion of accessible AI education. Adaptive learning platforms will continue to revolutionise personalised tutoring, and AI education for non-technical audiences will become more important. The democratisation of AI education presents both opportunities and risks, including oversaturation of information, potential misuse of AI technologies, skill gaps, privacy concerns, ethical dilemmas, and cultural insensitivity. Emphasising practical skills, continuous learning, and collaboration between government, industry, and academia are essential.