About this course
The maths behind machine learning, explained visually and in code. Every concept is tied to where it shows up — gradients in training, vectors in embeddings, probability in sampling.
What you will be able to do
- Work with vectors, matrices and transformations
- Reason with probability and distributions
- Use statistics to judge results
- Understand gradients and optimisation
Curriculum
01Linear algebra
- Vectors
- Matrices
- Eigen-things
02Probability
- Rules
- Distributions
- Bayes
03Statistics
- Estimation
- Testing
- Confidence
04Calculus
- Derivatives
- Gradients
- Optimisation
Tools you will use
Prerequisites
- School-level algebra