Overview:

This path equips learners with the mathematical foundation necessary to deeply understand and apply machine learning algorithms.

Who it’s for:

Learners with basic math knowledge who want to strengthen their understanding of core ML-related math concepts.

What you’ll learn:

• Linear Algebra

  • Vectors, matrices, and matrix operations

  • Dot products and linear transformations

• Calculus

  • Derivatives and gradients

  • Chain rule and partial derivatives

• Probability & Statistics

  • Basic probability theory and distributions

  • Bayes' theorem

  • Mean, variance, standard deviation

• Intro to optimization

  • Gradient descent and its role in ML training

What you’ll build:

• Hands-on notebooks using numpy to simulate mathematical operations

• Exercises that connect math with ML concepts like cost functions