Mathematics & Statistics for AI
The maths behind the models, taught visually — linear algebra, calculus, probability and statistics without the fear.
About this course
Most people bounce off AI because the maths was taught badly, not because it is beyond them. This course rebuilds the four pillars — linear algebra, calculus, probability, statistics — visually and computationally: every concept is drawn, animated, and then coded in Python so you see it work. By the end, gradient descent, matrices and distributions are things you can picture, not formulas you memorised.
Who it's for: Anyone whose school maths is rusty or scary, preparing for serious ML and deep learning study.
What you'll learn
Syllabus
Linear algebra
Weeks 1–2- Vectors and matrices
- Transformations visually
- Matrices in ML
Calculus
Weeks 3–4- Derivatives as slopes
- Gradients and optimisation
- Gradient descent by hand
Probability
Weeks 5–6- Thinking in probabilities
- Distributions
- Bayes intuition
Statistics & capstone
Weeks 7–8- Inference and testing
- Statistics in ML evaluation
- Mini-project
Tools you'll use
Certificate
Finish the course and its capstone project to earn a FuturAIse Academy Certificate of Completion — verifiable online, with the projects to back it up.