Guided routes
Learning paths
Same destination, different doors. Pick the description that sounds like you and follow the stops in order — each is a normal page you can leave and return to anytime.
Beginner math foundations
Starting from scratch — or rebuilding shaky ground.
Unknowns, shapes, and waves: the shared language every later course speaks. Finish this and nothing on the platform is closed to you.
Math for machine learning & data science
Here for ML/data science? This is your road — clearer than the textbooks.
The complete mathematical foundation for machine learning, in the right order: linear algebra (through SVD), the calculus of gradients, counting into probability and statistics, and the optimization that ties it all into how models learn. Each topic is rebuilt visually and intuitively. Recommended prerequisites: comfort with basic algebra and functions.
- 01Precalc refresh — variables & functions
- 02Linear algebra — vectors
- 03Dot product & similarity
- 04Norms & distance
- 05Matrices as transformations
- 06Rank & the four subspaces
- 07Eigenvectors
- 08Matrix decompositions & SVD
- 09Calculus — derivatives
- 10Partial derivatives & gradients
- 11Chain rule & backpropagation
- 12Counting — the probability bridge
- 13Random variables & distributions
- 14Conditional probability & Bayes
- 15Estimation, likelihood & MLE
- 16Correlation & regression
- 17Optimization — loss functions
- 18Gradient descent & learning rate
- 19Convexity & constraints
- 20Capstone — statistics for ML
Calculus & change
Ready for the platform's flagship course.
The full journey from functions to the fundamental theorem — derivatives, integrals, and the ideas that connect them.
Linear algebra & structure
For data, graphics, ML — the mathematics of many things at once.
Vectors to eigenvectors, with the projection geometry that powers compression, least squares, and (later) Fourier analysis.
Signals & systems
The engineering track: how devices hear, filter, and respond.
From “what is a signal?” to filters and frequency response — with the trig, complex-number, and oscillation bridges built in at the right moments.
Fourier & the frequency domain
The deep end: spectra, sampling, FFTs, and honest analysis.
Builds the frequency domain from linear-algebra geometry, makes it computable, and faces the real-world fine print of sampling and windowing.
Differential equations & dynamic systems
Predicting the future: growth, decay, oscillation, simulation.
Nature's laws as rate equations — read them, solve them, simulate them, and meet the resonance that connects them to signals.
Data, probability & uncertainty
For honest reasoning with data, measurements, and noise.
From coin flips to confidence to the noise floor of real instruments — statistics as self-defense and as engineering.
Applied math for engineering & computing
The grand tour: every pillar, pointed at real systems.
A long road for serious learners: foundations → calculus → linear algebra → dynamics → signals → frequency domain. Everything connects; this is the order it connects in.
Probability & statistics for data science
Turning data into trustworthy conclusions — and ML-ready intuition.
The data scientist's core: count, then reason about chance, then infer from samples, then model relationships — ending at the statistics behind machine learning. Heavy on figures and interpretation.
Engineering math
For engineers: the math behind systems, signals, and control.
The applied-engineering spine — linear algebra and calculus into differential equations, then signals, systems, and the frequency domain. Built around real engineering intuition.
Core university math
The standard first-year+ sequence, rebuilt for understanding.
The classic university core — single- and multivariable calculus, linear algebra, probability, and a first taste of differential equations — in a coherent, visual order.