Depends what level you want to reach. To use AI tools effectively at work: no maths required. To build applications using APIs and AI tools: basic Python is more useful than maths. To understand how models work conceptually: some linear algebra and statistics helps, but fast.ai has shown that starting with code and learning the maths when you need it is effective. To do original ML research or train models from scratch: yes — linear algebra, calculus, probability, and statistics are required. Most people learning AI for professional purposes fall in the first two categories, where maths is not a prerequisite.

It depends on your starting point and goal. AI literacy (using tools well): 2-4 weeks of daily practice. AI application (building with APIs, automation): 3-6 months of consistent learning and building. ML engineering (training models professionally): 1-2 years of structured learning and practice. The timeline accelerates dramatically if you have a real project — applying concepts immediately cements them far faster than course-only learning. The half-life of specific AI tools knowledge is short (things change fast), but understanding of fundamentals — what models can and can't do, how prompting works, how training shapes behaviour — remains durable.