Skip to content
LearnMathora

Twelve courses · 81 lessons · every level

Mathematics,
finally clear.

Learn mathematics visually, intuitively, and through real-life meaning. From algebra foundations to statistics, signals, and Fourier analysis — organized by difficulty, built for every level.

Explore all topicsFind a pathway
3.4

slope = the derivativearea = the integral

A taste of one course — calculus. Nine more await.

01The curriculum

Twelve subjects, one way of teaching — organized by level.

Browse the full curriculum →

Foundations

Start here — little or no prerequisites
Algebra foundationsEasyThe art of working with the unknown.4 lessonsGeometryEasySpace, shape, and the logic of seeing.4 lessonsTrigonometryEasyTriangles, circles, and everything that repeats.5 lessonsCounting & combinatoricsEasyThe bridge from foundations to probability.4 lessons

Core mathematics

The four pillars of the subject
CalculusMediumThe mathematics of change and accumulation.12 lessonsLinear algebraMediumThe mathematics of many things at once.9 lessonsProbability & statisticsMediumHonest reasoning under uncertainty.13 lessonsDiscrete mathematicsMediumThe mathematics of steps, networks, and code.4 lessons

Applied tracks

Where the pillars assemble into engineering
Differential equationsHardThe laws of nature, written as change.5 lessonsOptimization & gradient descentHardHow machines learn — calculus turned into training.5 lessonsSignals & systemsHardThe mathematics of everything that varies.8 lessonsFourier & the frequency domainHardEvery signal is a chord. Learn to read the notes.8 lessons

Featured pathway

Math for machine learning & data science

A complete, visual foundation — linear algebra through SVD, the calculus of gradients, counting into probability and statistics, and the optimization that ties it all into how models learn. Built to be clearer than the textbooks, with a figure on every page.

Follow the pathwayJump to SVD
  • Vectors, norms, rank & SVD
  • Gradients, chain rule & backprop
  • Counting → probability → Bayes
  • Estimation, regression & MLE
  • Loss functions & gradient descent

02Start where you are

Three levels. Every lesson labeled. No one left behind.

Every lesson on the platform carries a difficulty rating, so you can climb at your own pace — from intuitive first ideas to engineering-grade depth.

Easy

Algebra, geometry, and trigonometry — intuitive and prerequisite-free.

Begin with foundations →

Medium

Calculus, linear algebra, statistics, discrete math — solid intermediate reasoning.

Take on the pillars →

Hard

Differential equations, signals, and Fourier analysis — proof- and engineering-oriented.

Go deep →

03Featured course

Statistics — the mathematics of data and machine learning.

Thirteen lessons from first intuitions to regression and the statistics of ML: distributions, Bayes' theorem, estimation and likelihood, hypothesis testing, and how generalization, overfitting, and evaluation are statistics in disguise.

Start the statistics courseJump to Bayes & the base-rate trap
E[X]=∑x P(x)E[X] = \sum x\,P(x)

Expectation & distributions

P(C∣E)=P(E∣C)P(C)P(E)P(C \mid E) = \frac{P(E \mid C)P(C)}{P(E)}

Bayes' theorem

θ^=arg⁡max⁡θlog⁡L\hat{\theta} = \arg\max_\theta \log\mathcal{L}

Maximum likelihood

r=Cov(X,Y)σXσYr = \frac{\mathrm{Cov}(X,Y)}{\sigma_X\sigma_Y}

Correlation & regression

04Learn by doing

Nineteen interactives. Every big idea, touchable.

Drag a tangent along a wave. Watch a matrix bend space. Stack harmonics into a square wave. See aliasing fool a sampler, and the bell curve emerge from coin flips. Motion that teaches, never decorates.

Open the playground

05Check your understanding

66 practice questions, filterable by level.

Go to practice →

Concept checks from every lesson, gathered in one place with instant, kind explanations and live session mastery. Nothing graded — the only score is your own understanding.

06Guided pathways

Not sure where to start? Follow a road.

All 12 pathways →

Beginner math foundations

Starting from scratch — or rebuilding shaky ground.

7 stops →

Math for machine learning & data science

Here for ML/data science? This is your road — clearer than the textbooks.

20 stops →

Calculus & change

Ready for the platform's flagship course.

7 stops →

Linear algebra & structure

For data, graphics, ML — the mathematics of many things at once.

6 stops →

Signals & systems

The engineering track: how devices hear, filter, and respond.

9 stops →

Fourier & the frequency domain

The deep end: spectra, sampling, FFTs, and honest analysis.

9 stops →

Differential equations & dynamic systems

Predicting the future: growth, decay, oscillation, simulation.

7 stops →

Data, probability & uncertainty

For honest reasoning with data, measurements, and noise.

6 stops →

07Why it matters

Mathematics is already running your day.

All 16 applications →

Physics

Motion & velocity

Instantaneous speed — what the speedometer shows right now, not your trip average.

Physics

Acceleration

How quickly speed changes: the derivative of a derivative.

Physics

Falling objects

Exactly when and how fast it lands, from h(t) = h₀ − ½gt².

Biology

Population growth

Future population when growth is proportional to current size: exponential curves.

Epidemiology

Disease spread

When case curves accelerate, peak, and turn over — the inflection points health agencies watch.

Finance

Finance & compound growth

Continuous compounding (A = Pe^rt), present value, and how sensitive options are to price moves.

You were never “bad at math.”
It was badly explained.

Twelve courses, 81 lessons, every level. Every picture touchable, every idea connected to the world.

Explore all topicsStart from the beginning

Track progress, build a review queue, and pick up where you left off on your dashboard.