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Math: from algebra to machine learning

A free, step-by-step math course: algebra, geometry, calculus, linear algebra, probability and the math of machine learning. Every lesson has worked examples, a quiz, and an AI tutor you can ask about anything on the page.

1.Algebra

Variables, equations and functions: the language everything else is written in.

  1. 1Variables and expressions
  2. 2Solving linear equations
  3. 3Linear inequalities
  4. 4Systems of linear equationsComing soon
  5. 5Exponents and rootsComing soon
  6. 6Polynomials and factoringComing soon
  7. 7Quadratic equationsComing soon
  8. 8Functions and graphsComing soon
  9. 9Exponential functionsComing soon
  10. 10LogarithmsComing soon

2.Geometry and trigonometry

Shapes, angles and the unit circle.

  1. 1Angles and trianglesComing soon
  2. 2The Pythagorean theoremComing soon
  3. 3Similarity and areaComing soon
  4. 4The unit circleComing soon
  5. 5Trigonometric identitiesComing soon

3.Precalculus

Sequences, complex numbers and counting.

  1. 1Sequences and seriesComing soon
  2. 2Complex numbersComing soon
  3. 3Counting and combinatoricsComing soon

4.Calculus

Change and accumulation: limits, derivatives, integrals and their multivariable versions.

  1. 1LimitsComing soon
  2. 2DerivativesComing soon
  3. 3Applications of derivativesComing soon
  4. 4IntegralsComing soon
  5. 5Series and Taylor expansionsComing soon
  6. 6Partial derivatives and gradientsComing soon

5.Linear algebra

Vectors and matrices: the data structures of machine learning.

  1. 1VectorsComing soon
  2. 2Matrices and their operationsComing soon
  3. 3Determinants and inversesComing soon
  4. 4Eigenvalues and eigenvectorsComing soon
  5. 5Singular value decompositionComing soon

6.Probability and statistics

Reasoning with uncertainty.

  1. 1Probability basics
  2. 2Conditional probability and Bayes' theorem
  3. 3Random variables and distributions
  4. 4Expectation and variance
  5. 5Estimation and regressionComing soon

7.The math of machine learning

Optimization and the models built on everything above.

  1. 1Gradient descentComing soon
  2. 2Linear and logistic regressionComing soon
  3. 3BackpropagationComing soon
  4. 4Principal component analysisComing soon
  5. 5Neural networks and attentionComing soon

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