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Mathematics for AI

Exactly the mathematics you need to understand learning models, explained without needless formalism.

Course Duration: 6h​

What You'll Learn​

  • Read a matrix operation and understand what it does to data
  • Interpret a derivative as a direction of progress for a model
  • Follow the mechanics of gradient descent step by step
  • Handle conditional probability and Bayes' theorem
  • Recognize the role of bias and variance in a model's error

Prerequisites​

  • High-school mathematics
  • No formal proofs required

Course Modules​

  1. Vectors and matrices: the language of data
  2. Matrix product, transpose and inverse
  3. Norms, distances and cosine similarity
  4. Eigenvalues and dimensionality reduction
  5. Derivatives and gradient: the slope that guides learning
  6. Gradient descent and the learning rate
  7. Probability, independence and conditional probability
  8. Bayes' theorem and probabilistic reasoning
  9. Expectation, variance and common distributions
  10. The bias-variance trade-off, shown on an example

Assessment and certificate​

The course ends with a 40-question exam covering every module. On success, a certificate of completion is issued; its number is verifiable on the platform.

Free courses, by contrast, end with a 5-question quiz and a preview of the certificate, without certification.