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AI & Machine Learning
Mathematics for Machine Learning
The maths behind machine learning is far more approachable when you can see it. Drag vectors, watch matrices bend space in 3D, find the directions a dataset really varies in, follow gradients uphill and update beliefs with Bayes’ theorem — the exact ideas that models use every day.
What you’ll learn
- Use vectors, dot products and cosine similarity to reason about data
- Read matrices as transformations and interpret determinants
- Explain eigenvectors and use PCA to compress and visualize data
- Compute derivatives and gradients, and apply the chain rule
- Reason with probability, distributions and Bayes’ theorem
Curriculum
Module 1 · The core toolkit
5 lectures- 01Vectors and similarity3D modelFree16 min
- 02Matrices as transformations3D modelFree17 min
- 03Eigenvectors and PCA3D modelFree18 min
- 04Derivatives and gradients3D modelFree18 min
- 05Probability and Bayes’ theoremInteractiveFree18 min
Module 2 · Going deeper
5 lectures- 06Systems of equations and least squaresIncluded with the course
- 07Singular value decompositionIncluded with the course
- 08The chain rule and automatic differentiationIncluded with the course
- 09Distributions that matter in MLIncluded with the course
- 10Maximum likelihood estimationIncluded with the course
Module 3 · Putting it together
4 lectures- 11Convex optimizationIncluded with the course
- 12Information theory: entropy and cross-entropyIncluded with the course
- 13Statistics for model evaluationIncluded with the course
- 14The maths of a neural network, end to endIncluded with the course
Who this course is for
- Engineers starting a machine-learning journey
- Developers who want the maths behind ML without a university course
- Analysts refreshing their quantitative foundations
Before you start
School-level algebra. Everything else is built up visually from scratch.
Keep going
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Training a team on this?
We can combine Mathematics for Machine Learning with other courses into a learning path built around your team’s roles and goals.